Biomaker for ovarian and endometrial cancer: hepcidin

ABSTRACT

The present invention provides protein-based biomarkers and biomarker combinations that are useful in qualifying ovarian cancer status as well as endometrical cancer status in a patient. In particular, it has been found that hepcidin is a biomarker for both ovarian cancer and endometrial cancer and that a panel of biomarkers, including hepcidin, transthyretin and optionally other markers are useful to classify a subject sample as ovarian cancer or non-ovarian cancer. The biomarkers can be detected by SELDI mass spectrometry.

The present application claims the benefit of U.S. provisionalapplication No. 60/662,090 filed Mar. 11, 2005, which is incorporatedherein by reference in its entirety.

FIELD OF THE INVENTION

The invention relates generally to clinical diagnostics.

BACKGROUND OF THE INVENTION

Ovarian cancer is among the most lethal gynecologic malignancies indeveloped countries. Annually in the United States alone, approximately23,000 women are diagnosed with the disease and almost 14,000 women diefrom it. (Jamal, A., et al., CA Cancer J. Clin, 2002; 52:23-47). Despiteprogress in cancer therapy, ovarian cancer mortality has remainedvirtually unchanged over the past two decades. (Id.) Given the steepsurvival gradient relative to the stage at which the disease isdiagnosed, early detection remains the most important factor inimproving long-term survival of ovarian cancer patients.

The poor prognosis of ovarian cancer diagnosed at late stages, the costand risk associated with confirmatory diagnostic procedures, and itsrelatively low prevalence in the general population together poseextremely stringent requirements on the sensitivity and specificity of atest for it to be used for screening for ovarian cancer in the generalpopulation.

The identification of tumor markers suitable for the early detection anddiagnosis of cancer holds great promise to improve the clinical outcomeof patients. It is especially important for patients presenting withvague or no symptoms or with tumors that are relatively inaccessible tophysical examination. Despite considerable effort directed at earlydetection, no cost effective screening tests have been developed (PaleyP J., Curr Opin Oncol, 2001;13(5):399-402) and women generally presentwith disseminated disease at diagnosis. (Ozols R F, et al., Epithelialovarian cancer. In: Hoskins W J, Perez C A, Young R C, editors.Principles and Practice of Gynecologic Oncology. 3rd ed. Philadelphia:Lippincott, Williams and Wilkins; 2000. p. 981-1057).

The best-characterized tumor marker, CA125, is negative in approximately30-40% of stage I ovarian carcinomas and its levels are elevated in avariety of benign diseases. (Meyer T, et al., Br J Cancer,2000;82(9):1535-8; Buamah P., J Surg Oncol, 2000;75(4):264-5; Tuxen M K,et al., Cancer Treat Rev, 1995;21(3):215-45). Its use as apopulation-based screening tool for early detection and diagnosis ofovarian cancer is hindered by its low sensitivity and specificity.(MacDonald N D, et al., Eur J Obstet Gynecol Reprod Biol,1999;82(2):155-7; Jacobs I, et al., Hum Reprod, 1989;4(1):1-12; ShihI-M, et al., Tumor markers in ovarian cancer. In: Diamandis E P,Fritsche, H., Lilja, H., Chan, D. W., and Schwartz, M., editor. Tumormarkers physiology, pathobiology, technology and clinical applications.Philadelphia: AACC). Although pelvic, and more recently, vaginalsonography has been used to screen high-risk patients, neither techniquehas sufficient sensitivity and specificity to be applied to the generalpopulation. (MacDonald N D, et al., supra). Recent efforts in usingCA125 in combination with additional tumor markers (Woolas R P X F, etal., J Natl Cancer Inst, 1993;85(21):1748-51; Woolas R P, et al.,Gynecol Oncol, 1995;59(1):111-6; Zhang Z, et al., Gynecol Oncol,1999;73(1):56-61; Zhang Z, et al., Use of Multiple Markers to DetectStage I Epithelial Ovarian Cancers: Neural Network Analysis ImprovesPerformance. American Society of Clinical Oncology 2001; Annual Meeting,Abstract) in a longitudinal risk of cancer model (Skates S J, et al.,Cancer, 1995;76(10 Suppl):2004-10), and in tandem with ultrasound as asecond line test (Jacobs I D A, et al., Br Med J,1993;306(6884):1030-34; Menon U T A, et al., British Journal ofObstetrics and Gynecology, 2000;107(2):165-69) have shown promisingresults in improving overall test specificity, which is critical for adisease such as ovarian cancer that has a relatively low prevalence. Seealso Menon et al. J. Clin. Oncology (2005) 23(31):7919-26.

Due to the dismal prognosis of late stage ovarian cancer, it is thegeneral consensus that a physician will accept a test with a minimalpositive predictive value of 10%. (Bast, R. C., et al., Cancer Treatmentand Research, 2002; 107:61-97). Extending this to the generalpopulation, a general screening test would require a sensitivity greaterthan 70% and a specificity of 99.6%. Currently, none of the existingserologic markers, such as CA125, CA72-4, or M-CSF, individuallydelivers such a performance. (Bast, R. C., et al., Int J Biol Markers,1998; 13:179-87).

Thus, there is a critical need for new serological markers thatindividually or in combination with other markers or diagnosticmodalities deliver the required sensitivity and specificity for earlydetection of ovarian cancer. (Bast R C, et al., Early detection ofovarian cancer: promise and reality. Ovarian Cancer: ISIS Medical MediaLtd., Oxford, UK).

Given the low incidence of ovarian cancer, a screening test intended forthe asymptomatic woman with adequate positive predictive remainselusive. It has been demonstrated, however, that even in the absence ofa general screening test, one factor that does improve long-termsurvival of patients with ovarian cancer is appropriate triage to thespecialist gynecologic oncologist (Craig, C C et al, Effect of surgeonspecialty on processes of care and outcomes for ovarian cancer patients,J Natl Canc Inst, 2006: 98, 172-80). This is particularly true of womenwho present to their physician with symptoms suggestive of a pelvicmass.

Thus, it is desirable to have a reliable and accurate method ofdetermining the ovarian cancer status in patients, the results of whichcan then be used to manage subject treatment.

SUMMARY OF THE INVENTION

It has been found that hepcidin is a biomarker for ovarian cancer(invasive epithelial cancer). It has further been found that hepcidin isa biomarker that is differentially present in subjects havingendometrial cancer. More particularly, it has been found that thehepcidin level in a biological sample is increased in ovarian cancerversus non-ovarian cancer and in endometrial cancer versusnon-endometrial cancer. Put another way, elevated hepcidin levels arecorrelated with ovarian cancer and with endometrial cancer.

In certain embodiments, the disease statuses to be distinguished are:ovarian cancer versus benign ovarian disease; ovarian cancer versusbenign gynecologic disease; ovarian cancer versus a gynecologicalcondition selected from endometriosis, uterine fibroma, breast cancerand cervical cancer; ovarian versus other malignancy (e.g., breastcancer or colon cancer); stage I ovarian cancer versus non-ovariancancer, and recurrence of ovarian cancer versus non-ovarian cancer.Based on the status determined, further procedures may be indicated,including additional diagnostic tests or therapeutic procedures orregimens.

It has further been found that when hepcidin level is used incombination with the level of other biomarkers, the predictive power ofthe diagnostic test is improved. More specifically, increased levels ofhepcidin and decreased levels transthyretin are correlated with ovariancancer. Increased levels of hepcidin and decreased levels oftransthyretin, together with levels of one or more of Apo A1 (decreasedlevel), transferrin (decreased level), CTAP-III (elevated level) and aninternal fragment of ITIH4 (elevated level) also are correlated withovarian cancer. These biomarkers can be further combined with β-2microglobulin (elevated level), CA125 (elevated level) and/or otherknown ovarian cancer biomarkers in the diagnostic test.

In one aspect, the present invention provides methods for qualifyingovarian cancer status in a subject comprising measuring one or morebiomarkers in a biological sample from the subject, wherein at least onebiomarker is hepcidin, and correlating the measurement or measurementswith an ovarian cancer status selected from ovarian cancer andnon-ovarian cancer. In one embodiment of such methods, a plurality ofbiomarkers in the biological sample are measured, wherein the measuredbiomarkers further comprise transthyretin in addition to hepcidin. Inanother embodiments of such methods, a plurality of biomarkers in thebiological sample are measured, wherein the measured biomarkers furthercomprise in addition to hepcidin at least one biomarker selected fromthe group consisting of: Apo A1, transferrin, CTAP-III and ITIH4fragment. In a further aspect of such methods, a plurality of biomarkersin the biological sample are measured, wherein the measured biomarkersfurther comprise in addition to hepcidin at least two biomarkersselected from the group consisting of Apo A1, transferrin, CTAP-III andITIH4 fragment. In a yet further aspect of such methods, a plurality ofbiomarkers in the biological sample are measured, wherein the measuredbiomarkers further comprise in addition to hepcidin at least threebiomarkers selected from the group consisting of Apo A1, transferrin,CTAP-III and ITIH4 fragment. In a still further aspect, a plurality ofbiomarkers are measured, and the measured biomarkers comprise β-2microglobulin.

In one embodiment, hepcidin may be hepcidin-25, transthyretin may becysteinylated transthyretin, and/or ITIH4 fragment may be ITIH4 fragment1.

In another embodiment, one or more biomarkers are measured by massspectrometry. The mass spectrometry suitably may be SELDI-MS. In afurther aspect, one or more biomarkers are measured by immunoassay.

A variety of biological samples may be employed in methods of theinvention, including e.g. where the biological sample comprises blood ora blood derivative, or where the biological sample comprises ovariancyst fluid, ascites, or urine.

In one embodiment of methods of the invention, wherein non-ovariancancer is benign ovarian disease. In another embodiment, non-ovariancancer is a gynecological condition such as benign ovarian cyst,endometriosis, uterine fibroma, breast cancer and cervical cancer. In afurther embodiment, the ovarian cancer is stage I or II ovarian cancer.In certain aspects, the subject has been treated for ovarian cancer andthe ovarian cancer is recurrence of cancer.

In another aspect, methods are provided for qualifying endometrialcancer status is a subject comprising (a) measuring one or morebiomarkers in a biological sample from the subject, wherein at least onebiomarker is hepcidin; and (b) correlating the measurement ormeasurements with endometrial cancer status. In one embodiment, thestatus is endometrial cancer versus non-cancer.

Methods of the invention may further comprises reporting the status tothe subject, recording the status on a tangible medium, and/or managingsubject treatment based on the status. One or more biomarker may beafter subject management and the measurement correlated with diseaseprogression.

In a preferred aspect, methods are provided for determining the courseof ovarian cancer comprising (a) measuring, at a first time, one or morebiomarkers in a biological sample from the subject, wherein at least onebiomarker is hepcidin; (b) measuring, at a second time, at least onebiomarker in a biological sample from the subject; and (c) comparing thefirst measurement and the second measurement; wherein the comparativemeasurements determine the course of the ovarian cancer.

In a further preferred aspect, methods are provided that comprisemeasuring hepcidin and transthyretin in a sample from a subject. Incertain embodiments, such methods may further comprise measuring atleast one of Apo A1, transferrin, CTAP-III and ITIH4 fragment in thesample.

In another embodiment, the invention provides a kit that comprises (a) asolid support comprising at least one capture reagent attached thereto,wherein the capture reagent binds hepcidin; and (b) instructions forusing the solid support to detect hepcidin. The solid support maycomprise e.g. a SELDI probe. The kit also may optionally comprise astandard reference of hepcidin.

In a further embodiment, the invention provides a kit that comprises (a)at least one solid support comprising at least one capture reagentattached thereto, wherein the capture reagent binds or reagents bindhepcidin and transthyretin; and (b) instructions for using the solidsupport or supports to detect hepcidin and transthyretin. The solidsupport may comprise e.g. a SELDI probe. The kit also may optionallycomprise a standard reference of hepcidin and transthyretin.

In a yet further embodiment, the invention provides a kit that comprises(a) at least one solid support comprising at least one capture reagentattached thereto, wherein the capture reagent or reagents bind hepcidin,transthyretin and at least one of Apo A1, transferrin, CTAP-III andITIH4 fragment; and (b) instructions for using the solid support orsupports to detect hepcidin, transthyretin and at least one of Apo A1,transferrin, CTAP-III and ITIH4 fragment. The solid support may comprisee.g. a SELDI probe. The kit also may optionally comprise a standardreference of hepcidin and transthyretin and at least one of Apo A1,transferrin, CTAP-III and ITIH4 fragment.

The invention further includes software products that comprise (a) codethat accesses data attributed to a sample, the data comprisingmeasurement of at least one biomarker in the sample, wherein at leastone biomarker is hepcidin; and (b) code that executes a classificationalgorithm that classifies the ovarian cancer status of the sample as afunction of the measurement. In one aspect, the at least one biomarkerfurther comprises transthyretin. In another aspect, the at least onebiomarker further comprises at least one biomarker selected from Apo A1,transferrin, CTAP-III and ITIH4 fragment. In a yet further aspect, theat least one biomarker further comprises β2-microglobulin.

The invention also provides methods comprising communicating to asubject a diagnosis relating to ovarian cancer status determined fromthe correlation of at least one biomarker in a sample from the subject,wherein at least one biomarker is hepcidin. In one aspect, the at leastone biomarker further comprises transthyretin. The diagnosis may besuitably communicated to the subject e.g. via a computer-generatedmedium.

The invention further provides methods for identifying a compound thatinteracts with hepcidin, wherein said method comprises a) contactinghepcidin with a test compound; and b) determining whether the testcompound interacts with hepcidin.

Other aspects of the invention are discussed infra.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows ROC curve analysis showing the power of the hepcidin peakat m/z 2789 has in differentiating ovarian cancer from healthy controls.The AUC is 0.876 and is significantly greater than 0.5 withp-value<0.0001.

FIG. 2 shows the sequences of various hepcidin fragments, including thefour correlated fragments, hepcidin-25 (SEQ ID NO:13), hepcidin-24 (SEQID NO: 14), hepcidin-22 (SEQ ID NO: 16), and hepcidin-20 (SEQ ID NO:18). The 21 sequences are numbered SEQ ID NO: 1 through SEQ ID NO: 22from top to bottom.

FIG. 3 shows the SELDI spectrum of the serum sample afterimmunoprecipitation/pull-down using an antibody against ITIH4 fragment(m/z 3272). Peaks with rectangle labels are known fragments of ITIH4.The four discovered hepcidin variants are in this spectrum atapproximate m/z locations 2191, 2436, 2673, and 2788 (indicated byarrows).

FIG. 4 shows ROC curve analysis showing the power of the hepcidin peakat m/z 2789 has in differentiating ovarian cancer from other cancers.The AUC is 0.774 and is significantly greater than 0.5 withp-value<0.0001.

FIG. 5 shows ROC curve analysis showing the power of the hepcidin peakfor the two independent validation sets. The AUCs are 0.756 and 0.772,both greater than 0.5 with p-value<0.0001.

FIG. 6 shows a scatterplot of the five groups of samples in two of thefour peaks representing hepcidin variants.

FIG. 7 shows a scatterplot of five groups of patients from anindependent validation set using two of the hepcidin peaks. It showsthat these peaks are lower in patients free of cancer and patients aftertreatment, and are higher in patients with ovarian cancer pretreatment,as well as in those with recurrent ovarian cancer. The hepcidin levelcorrelates with the tumor load.

FIG. 8 shows a scatterplot of five groups of patients from a secondindependent validation set using two of the hepcidin peaks. It showsthat these peaks are lower in healthy controls and patients with benigndiseases, and are higher in patients with ovarian cancer.

FIGS. 9A-9G show SELDI mass spectra displaying various biomarkersmentioned herein. FIG. 9A shows ITIH4 fragment 1 captured on an IMAC-50biochip charged with copper. FIG. 9B shows hepcidin-25 captured on anIMAC-50 biochip charged with copper. FIG. 9C shows CTAP-III captured onan IMAC-50 biochip charged with copper. FIG. 9D shows β2 microglobulincaptured on an IMAC-50 biochip charged with copper. FIG. 9E showstransthyretin captured on a Q-10 biochip. FIG. 9F shows Apo A1 capturedon an H50 biochip. FIG. 9G shows transferrin captured on an IMAC-50biochipcharged with copper.

FIG. 10 shows a close-up of a mass spectrum of forms of transthyretin inserum.

FIG. 11 shows ROC curve analysis showing the mini-assay of Example 4,which follows.

DETAILED DESCRIPTION OF THE INVENTION 1. Introduction

A biomarker is an organic biomolecule which is differentially present ina sample taken from a subject of one phenotypic status (e.g., having adisease) as compared with another phenotypic status (e.g., not havingthe disease). A biomarker is differentially present between differentphenotypic statuses if the mean or median expression level of thebiomarker in the different groups is calculated to be statisticallysignificant. Common tests for statistical significance include, amongothers, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and oddsratio. Biomarkers, alone or in combination, provide measures of relativerisk that a subject belongs to one phenotypic status or another. Assuch, they are useful as markers for disease (diagnostics), therapeuticeffectiveness of a drug (theranostics) and of drug toxicity.

Biomarkers of this invention were discovered using SELDI. Accordingly,they are characterized, in part, by their mass-to-charge ratio, theshape of the peak in a mass spectrum and their binding characteristics.These characteristics represent inherent characteristics of thebiomolecule and not process limitations in the manner in which thebiomolecule is discriminated.

Biomarkers of this invention are characterized in part by theirmass-to-charge ratio. The mass-to-charge ratio of each biomarker isprovided herein. A particular molecular marker designated, for example,as “M2789” has a measured mass-to-charge ratio of 2789 D. Themass-to-charge ratios were determined from mass spectra generated on aCiphergen Biosystems, Inc. PBS II mass spectrometer or a Ciphergen PCS4000 mass spectrometer. The PBS II is instrument has a mass accuracy ofabout +/−0.15 percent. Additionally, the instrument has a massresolution of about 400 to 1000 m/dm, where m is mass and dm is the massspectral peak width at 0.5 peak height. The PCS4000 instrument has amass accuracy of about +/−0.12% raw data with an expected externallycalibrated mass accuracy of 0.1% and internally calibrated mass accuracyof 0.01%. Additionally, the instrument has a mass resolution of about1000 to 2000 m/dm, where m is mass and dm is the mass spectral peakwidth at 0.5 peak height. The mass-to-charge ratio of the biomarkers wasdetermined using Biomarker Wizard™ software (Ciphergen Biosystems,Inc.). Biomarker Wizard assigns a mass-to-charge ratio to a biomarker byclustering the mass-to-charge ratios of the same peaks from all thespectra analyzed, as determined by the PBSII or PCS4000, taking themaximum and minimum mass-to-charge-ratio in the cluster, and dividing bytwo. Accordingly, the masses provided reflect these specifications.

Biomarkers of this invention are further characterized by the shape oftheir spectral peak in time-of-flight mass spectrometry. Mass spectrashowing peaks representing the biomarkers are presented in the Figures.

Biomarkers of this invention also are characterized by their bindingcharacteristics to adsorbent surfaces. The binding characteristics ofeach biomarker also are described herein.

2. Biomarkers for Ovarian Cancer 2.1. Hepcidin

Hepcidin was originally identified as a 25 amino acid peptide(hepcidin-25) in human plasma and urine, exhibiting antimicrobialactivity. The full-length hepcidin precursor is an 84 amino acid protein(SwissProt Accession No. P81172) comprising a signal sequence and apro-region (see Kulaksiz, H. et al. (2004) Gut 53:735-743). The hepcidinbiomarkers of the present invention are derived from the C-terminus ofthe full-length hepcidin protein. Hepcidin is recognized by antibodiesavailable from, e.g., U.S. Biological (catalog H2008-51) (www.usbio.net,Swampscott, Mass.). Four different variants of hepcidin useful asbiomarkers of this invention are characterized by calculatedmass-to-charge ratios of 2789, 2673, 2436, and 2191.

Hepcidin was discovered to be a biomarker for ovarian and endometrialcancer using SELDI technology employing ProteinChip arrays fromCiphergen Biosystems, Inc. (Fremont, Calif.) (“Ciphergen”). Morespecifically, hepcidin levels can distinguish ovarian cancer from eachof non-cancer, cervical cancer and benign ovarian disease. It also candistinguish between endometrial cancer and non-cancer. Urine and serumsamples were collected from subjects diagnosed with ovarian cancer,endometrial cancer, cervical cancer and subjects diagnosed as normal oras having benign disease. The samples were applied to SELDI biochips,with or without co-immunoprecipitation with the ITIH4 3272 m/z fragment(see International Publication Number WO 2004/099432), using an antibodyraised against ITIH4 fragment 1 (discussed in more detail below), andspectra of polypeptides in the samples were generated by time-of-flightmass spectrometry on a Ciphergen PBSIIc or PCS4000 mass spectrometer.The spectra thus obtained were analyzed by Ciphergen Express™ DataManager Software with Biomarker Wizard and Biomarker Pattern Softwarefrom Ciphergen Biosystems, Inc. The mass spectra for each group weresubjected to scatter plot analysis. A Mann-Whitney test analysis wasemployed to compare ovarian cancer and control groups for each proteincluster in the scatter plot, and proteins were selected that differedsignificantly (p<0.01) between the two groups. This method is describedin more detail in the Example Section.

Specific biomarkers thus discovered are presented in Table 1. The“ProteinChip assay” column refers to chromatographic fraction in whichthe biomarker is found, the type of biochip to which the biomarker bindsand the wash conditions, as per the Examples. In each case, thebiomarkers each may be found using a variety of alternate ProteinChipassays. The “theoretical mass” provides the expected mass based on aminoacid sequence and modifications such as disulfide bonds.

TABLE 1 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay Hepcidin-25 M2789 0.002 Up Urine, CM10, wash with 100(theoretical mass = mM sodium acetate, pH 4 2789.41 D) 0.0011 Up Urine,IMAC30-CU⁺⁺, wash with 100 mM sodium phosphate, 0.5 M NaCl, pH 70.0000069 Up Serum, IMAC30-Cu⁺⁺, wash with 50 mM sodium phosphatebuffer, 205 mM NaCl, pH 6.0 Sample set 1 discovery Up Serum,immunoprecipitate ovarian Cancer vs. ITIH4 (3272 m/z fragment), control:0.001040 wash with PBS/0.1% Triton, ovarian Cancer vs. elute withorganic buffer, other cancers: 0.000002 IMAC-Cu⁺⁺, wash with organicbuffer Sample set 2 Up Serum, immunoprecipitate Validation 0.000007ITIH4 (3272 m/z fragment), wash with PBS/0.1% Triton, elute with organicbuffer, IMAC-Cu⁺⁺, wash with organic buffer Sample set 3 Up Serum,immunoprecipitate Validation 0.000000 ITIH4 (3272 m/z fragment), washwith PBS/0.1% Triton, elute with organic buffer, IMAC-Cu⁺⁺, wash withorganic buffer Hepcidin-24 M2673 0.001 Up Urine, CM10, wash with 100(theoretical mass = mM sodium acetate, pH 4 2674.32 D) 0.01 Up Urine,IMAC30-Cu⁺⁺, wash with 100 mM sodium phosphate, 0.5 M NaCl, pH 7 Sampleset 1 discovery Up Serum, immunoprecipitate ovarian Cancer vs. ITIH4(3272 m/z fragment), control: 0.000009 wash with PBS/0.1% Triton,ovarian Cancer vs. elute with organic buffer, other cancer: 0.000002IMAC-Cu⁺⁺, wash with organic buffer Sample set 2 Up Serum,immunoprecipitate Validation 0.000097 ITIH4 (3272 m/z fragment), washwith PBS/0.1% Triton, elute with organic buffer, IMAC-Cu++, wash withorganic buffer Sample set 3 Up Serum, immunoprecipitate Validation0.000001 ITIH4 (3272 m/z fragment), wash with PBS/0.1% Triton, elutewith organic buffer, IMAC-Cu⁺⁺, wash with organic buffer Hepcidin-22M2436 0.0002 Up Urine, CM10, wash with 100 (theoretical mass = mM sodiumacetate, pH 4 2436.07) 0.0619 Up Urine, IMAC30-Cu⁺⁺, wash with 100 mMsodium phosphate, 0.5 M NaCl, pH 7 Sample set 1 discovery Up Serum,immunoprecipitate ovarian Cancer vs. ITIH4, wash with PBS/0.1% control:0.000030 Triton, elute with organic buffer, ovarian Cancer vs.IMAC-Cu⁺⁺, wash with organic other cancer: 0.000015 buffer Sample set 2Up Serum, immunoprecipitate Validation 0.002027 ITIH4 (3272 m/zfragment), wash with PBS/0.1% Triton, elute with organic buffer,IMAC-Cu⁺⁺, wash with organic buffer Sample set 3 Up Serum,immunoprecipitate Validation 0.000000 ITIH4 (3272 m/z fragment), washwith PBS/0.1% Triton, elute with organic buffer, IMAC-Cu⁺⁺, wash withorganic buffer Hepcidin-20 M2191 0.0061 Up Urine, CM10, wash with 100(theoretical mass = mM sodium acetate, pH 4 2191.78) 0.0023 Up Urine,IMAC30-Cu⁺⁺, wash with 100 mM sodium phosphate, 0.5 M NaCl, pH 7 Sampleset 1 discovery Up Serum, immunoprecipitate ovarian Cancer vs. ITIH4(3272 m/z fragment), control: 0.000009 wash with PBS/0.1% Triton,ovarian Cancer vs. elute with organic buffer, other cancer: 0.000007IMAC-Cu⁺⁺, wash with organic buffer Sample set 2 Up Serum,immunoprecipitate Validation 0.020419 ITIH4 (3272 m/z fragment), washwith PBS/0.1% Triton, elute with organic buffer, IMAC-Cu⁺⁺, wash withorganic buffer Sample set 3 Up Serum, immunoprecipitate Validation0.000000 ITIH4 (3272 m/z fragment), wash with PBS/0.1% Triton, elutewith organic buffer, IMAC-Cu⁺⁺, wash with organic buffer

The amino acid sequences of hepcidin-25, -24, -22 and -20 are:

Hepcidin-25 (SEQ ID NO: 13): DTHFPICIFCCGCCHRSKCGMGCKT Hepcidin-24 (SEQID NO: 14):  THFPICIFCCGCCHRSKCGMCCKT Hepcidin-22 (SEQ ID NO: 16):   FPICIECCGCCHRSKCGMCCKT Hepcidin-20 (SEQ ID NO: 18):     ICIFCCGCCHRSKCGMCCKT

The biomarkers of this invention are further characterized by theirbinding properties on chromatographic surfaces. Hepcidin binds to cationexchange adsorbents (e.g., the Ciphergen® CM10 ProteinChip® array) afterwashing with 100 mM sodium acetate at pH 4. Hepcidin also binds to metalchelate adsorbents (e.g., the Ciphergen® IMAC-Cu⁺⁺ ProteinChip® array)after washing with 100 mM sodium phosphate, 0.5 M NaCl, pH 7 or organicbuffer. Hepcidin may be visualized in the same assay as used tovisualize ITIH4, as described below.

The preferred biological sources for detection of hepcidin is urine orserum. Hepcidin may also be detected in ascites fluid and cyst fluid,tissues and organs such as liver, and in specific cells, such asmacrophages.

2.2. Transthyretin

Transthyretin, also called “pre-albumin” is another biomarker that isuseful in the methods of the present invention. Transthyretin andvariants thereof are described as biomarkers for ovarian cancer in USpatent publication 2005-0059013 A1 and International Patent PublicationWO 2005/098447. Unmodified transthyretin is a 127 amino acid proteinderiving from a 147 amino acid precursor (SwissProt Accession No.P02766). The transthyretin biomarkers of the present invention includeany or all of unmodified transthyretin and various modified forms.Transthyretin is recognized by antibodies available from, e.g., Dako(catalog A0002) (www.dako.com, Glostrup, Denmark).

In mass spectra of serum, transthyretin appears as a cluster of peaksaround 13.9K Daltons. This cluster includes several forms oftransthyretin including unmodified transthyretin, S-sulfonatedthransthyretin, S-cysteinylated transthyretin, S-Gly-Cys transthyretinand S-glutathionylated transthryetin. Any and/or all of these is usefulas a biomarker for ovarian cancer. However, the S-cysteinylated versionrepresents the dominant form in the spectrum and is a preferredbiomarker when using mass spectrometry. Another variant of transthyretinuseful as a biomarker is transthyretin ΔN10. Specific transthyretinbiomarkers thus discovered are presented in Table 2.

TABLE 2 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay transthyretin ΔN10 p < 0.001 Down (ovarian v. Q10 arrayusing 100 mM (M12,870.9) (predicted non-ovarian) Sodium Phosphatebuffer, pH mass 12,887 daltons) 7.0 (PB buffer) unmodified transthyretinp < 0.001 Down (ovarian v. Q10 array using 100 mM (M13900) (predictednon-ovarian) Sodium Phosphate buffer, pH mass 13,761 daltons) 7.0 (PBbuffer) sulfonated transthyretin p < 0.001 Down (ovarian v. Q10 arrayusing 100 mM (M13850) (predicted non-ovarian) Sodium Phosphate buffer,pH mass 13,841 daltons) 7.0 (PB buffer) cysteinylated transthyretin p <0.001 Down (ovarian v. Q10 array using 100 mM (M13,890.8) (predictednon-ovarian) Sodium Phosphate buffer, pH mass 13,880 daltons) 7.0 (PBbuffer) CysGly modified p < 0.001 Down (ovarian v. Q10 array using 100mM transthyretin non-ovarian) Sodium Phosphate buffer, pH (M13944)(predicted 7.0 (PB buffer) mass 13,937 daltons) glutathionylated p <0.001 Down (ovarian v. Q10 array using 100 mM transthyretin non-ovarian)Sodium Phosphate buffer, pH (M14,086.9) (predicted 7.0 (PB buffer) mass14,066 daltons)

2.3. ApoA1

Another biomarker that is useful in the methods of the present inventionis apolipoprotein A1, also referred to as Apo A1. Apo A1 is described asa biomarker for ovarian cancer in US patent publication 2005-0059013 A1and International Patent Publication WO 2005/098447. Apo A1 is a 243amino acid protein derived from a 267 amino acid precursor (SwissProtAccession No. P02647). Apo A1 is recognized by antibodies availablefrom, e.g., EMD Biosciences, Inc. (catalog 178474)(www.emdbiosciences.com/home.asp, San Diego, Calif.). Specific Apo A1biomarkers are presented in Table 3. ApoA1 can be visualized on H50arrays or IMAC30 or IMAC50 arrays, but is preferentially visualized onH50 arrays.

TABLE 3 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay Apo A1 (M28043) <.000001 Down H50 buffer (10% (predictedmass: acetonitrile, 28,078.62 D) 0.1% TFA IMAC Cu⁺⁺ ApoA1 variant DownH50 buffer (10% (M29977.4) acetonitrile, (appears as 0.1% TFA shoulderto peak at 28,043 D)

Preferred methods of the present invention include the use of modifiedforms of Apo A1. Modification of Apo A1 may include thepost-translational addition of various chemical groups, for example,glycosylation and lipidation.

2.4. Transferrin

Another biomarker that is useful in the methods of the present inventionis transferrin. Transferrin is described as a biomarker for ovariancancer in US patent publication 2005-0214760 A1. Transferrin is a 679amino acid protein derived from a 698 amino acid precursor (GenBankAccession No. NP_(—)001054 GI:4557871; SwissProt Accesion No. P02787).Transferrin is recognized by antibodies available from, e.g., Dako(catalog A006) (www.dako.com, Glostrup, Denmark). Transferrin isglycosylated. Therefore, the measured molecular weight is higher thanthe theoretical weight, which does not take glycosylation into account.Specific transferrin biomarkers are presented in Table 4.

TABLE 4 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay transferrin <0.0001 Down IMAC-Ni (M79K) 100 mM Na₂HPO₄(predicted mass: pH 6.0 IMAC-Cu 75,181 D) 50 mM Na phosphate 0.25M NaClpH 6.0

2.5. CTAP-III

Another biomarker that is useful in the methods of the present inventionis CTAP-III (connective tissue activating peptide III), derived fromplatelet basic protein. CTAP-III is described as a biomarker for ovariancancer in U.S. provisional patent application 60/693,324, filed Jun. 22,2005 (Zhang et al.). CTAP-III is an 85 amino acid protein (SwissProtP02775). CTAP-III is recognized by antibodies available from, e.g.,Chemicon International (catalog 1484P) (www.chemicon.com, Temecula,Calif.) CTAP-III is a fragment of platelet basic protein and includesamino acids 44-128 of platelet basic protein. The specific CTAP-IIIbiomarker is presented in Table 5.

TABLE 5 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay CTAP-III <0.0001 Up IMAC-Cu⁺⁺ (M9290) 100 mM Na (predictedmass: phosphate, pH 7.0 9287.74 D)

2.6. ITIH4 Fragment

Other biomarkers that are useful in the methods of the present inventionone or more of a closely related set of cleavage fragments ofinter-α-trypsin inhibitor heavy chain H4 precursor, also referred toalternatively herein as “ITIH4 fragments.” ITIH4 fragments are describedas biomarkers for ovarian cancer in US patent publication 2005-0059013A1, International Patent Publication WO 2005/098447 and Fung et al.,Int. J. Cancer 115:783-789 (2005). ITIH4 fragments can be selected fromthe group consisting of ITIH4 fragment no. 1, ITIH4 fragment no. 2, andITIH4 fragment no. 3. Specific ITIH4 internal fragment biomarkers arepresented in Table 6.

TABLE 6 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay ITIH4 fragment 1 <0.01 Up IMAC-Cu⁺⁺ (M3272) 100 mM Na(predicted mass: phosphate, pH 7.0 3273.72 D) ITIH4 fragment 2 <0.02 UpIMAC-Cu⁺⁺ (M2725) 100 mM Na (predicted mass: phosphate, pH 7.0 2725.06D) ITIH4 fragment 3 <0.0057 Up IMAC-Cu⁺⁺ (M2627) 100 mM Na (predictedmass: phosphate, pH 7.0 2627.94 D)

The amino acid sequences of the ITIH4 fragments were determined to be:

IT1H4 fragment 1 (SEQ ID NO: 23): MNERPGVLSSRQLGLPGPPDVPDHAAYHPF ITIH4fragment 2 (SEQ ID NO: 24): PGVLSSRQLGLPGPPDVPDHAAYHPF ITIH4 fragment 3(SEQ ID NO: 25): GVLSSRQLGLPGPPDVPDHAAYHPF

ITIH4 precursor is a 930 amino acid protein (SwissProt Q14624). ITIH4fragment 1 spans amino acids 658-687 of human ITIH4 precursor. ITIH4fragment 2 spans amino acids 662-687 of ITIH4 precursor. ITIH4 fragment3 spans amino acids 663-687 of ITIH4 precursor.

Additionally, preferred methods of the present invention include the useof modified forms of ITIH4 fragment. Modification of ITIH4 fragment mayinclude the post-translational addition of various chemical groups, forexample, glycosylation, lipidation, cysteinylation, andglutathionylation.

2.7. β-2 Microglobulin

Another biomarker that is useful in the methods of the present inventionis β2-microglobulin. β2-microglobulin is described as a biomarker forovarian cancer in U.S. provisional patent publication 60/693,679, filedJun. 24, 2005 (Fung et al.). β2-microglobulin is a 99 amino acid proteinderived from an 119 amino acid precursor (GI:179318; SwissProt AccessionNo. P61769). β2-microglobulin is recognized by antibodies availablefrom, e.g., Abcam (catalog AB759) (www.abcam.com, Cambridge, Mass.).Specific β2-microglobulin biomarkers are presented in Table 7.

TABLE 8 Up or down regulated in ProteinChip ® Marker P-Value ovariancancer assay β2-microglobulin <0.0001 Up IMAC-Cu⁺⁺ (M11.7K) (predictedmass: 11729.17 D)

3. Biomarkers and Different Forms of a Protein

Proteins frequently exist in a sample in a plurality of different forms.These forms can result from either or both of pre- andpost-translational modification. Pre-translational modified formsinclude allelic variants, splice variants and RNA editing forms.Post-translationally modified forms include forms resulting fromproteolytic cleavage (e.g., fragments of a parent protein),glycosylation, phosphorylation, lipidation, oxidation, methylation,cysteinylation, sulphonation and acetylation. When detecting ormeasuring a protein in a sample, the ability to differentiate betweendifferent forms of a protein depends upon the nature of the differenceand the method used to detect or measure. For example, an immunoassayusing a monoclonal antibody will detect all forms of a proteincontaining the epitope and will not distinguish between them. However, asandwich immunoassay that uses two antibodies directed against differentepitopes on a protein will detect all forms of the protein that containboth epitopes and will not detect those forms that contain only one ofthe epitopes. In diagnostic assays, the inability to distinguishdifferent forms of a protein has little impact when the forms detectedby the particular method used are equally good biomarkers as anyparticular form. However, when a particular form (or a subset ofparticular forms) of a protein is a better biomarker than the collectionof different forms detected together by a particular method, the powerof the assay may suffer. In this case, it is useful to employ an assaymethod that distinguishes between forms of a protein and thatspecifically detects and measures a desired form or forms of theprotein. Distinguishing different forms of an analyte or specificallydetecting a particular form of an analyte is referred to as “resolving”the analyte.

Mass spectrometry is a particularly powerful methodology to resolvedifferent forms of a protein because the different forms typically havedifferent masses that can be resolved by mass spectrometry. Accordingly,if one form of a protein is a superior biomarker for a disease thananother form of the biomarker, mass spectrometry may be able tospecifically detect and measure the useful form where traditionalimmunoassay fails to distinguish the forms and fails to specificallydetect to useful biomarker.

One useful methodology combines mass spectrometry with immunoassay.First, a biospecific capture reagent (e.g., an antibody, aptamer orAffibody that recognizes the biomarker and other forms of it) is used tocapture the biomarker of interest. Preferably, the biospecific capturereagent is bound to a solid phase, such as a bead, a plate, a membraneor a chip. After unbound materials are washed away, the capturedanalytes are detected and/or measured by mass spectrometry. (This methodalso will also result in the capture of protein interactors that arebound to the proteins or that are otherwise recognized by antibodies andthat, themselves, can be biomarkers.) Various forms of mass spectrometryare useful for dectecting the protein forms, including laser desorptionapproaches, such as traditional MALDI or SELDI, and electrosprayionization.

Thus, when reference is made herein to detecting a particular protein orto measuring the amount of a particular protein, it means detecting andmeasuring the protein with or without resolving various forms ofprotein. For example, the step of “measuring hepcidin” includesmeasuring hepcidin by means that do not differentiate between variousforms of the protein (e.g., certain immunoassays) as well as by meansthat differentiate some forms from other forms or that measure aspecific form of the protein (e.g., any and/or all of hepcidin-25,hepcidin-24, hepcidin-22 and hepcidin-20, individually or incombination). In contrast, when it is desired to measure a particularform or forms of a protein, e.g., a particular form of hepcidin, theparticular form (or forms) is specified. For example, “measuringhepcidin-25” means measuring hepcidin-25 in a way that distinguishes itfrom other forms of hepcidin, e.g., hepcidin-24, hepcidin-22 andhepcidin-20. Similarly, reference to “measuring transthyretin” includesmeasuring any and/or all forms of transthyretin found in a subject testsample, individually or in combination, while reference to “measuringcysteinylated transthyretin” means measuring transthryetin in a way thatallows one to distinguish cysteinylated transthyretin from other formsof transthyretin found in a patient sample, e.g., transthyretin ΔN10,unmodified transthyretin, glutathionylated transthryetin, sulfonatedtransthryetin, etc. “Measuring un-cleaved transthyretin” means measuringany individual or combination of unmodified transthyretin, sulfonatedtransthryetin, cysteinylated transthyretin, CysGly modifiedtransthyretin and glutathionylated transthryetin.

4. Detection of Biomarkers for Ovarian Cancer

The biomarkers of this invention can be detected by any suitable method.Detection paradigms include optical methods, electrochemical methods(voltametry and amperometry techniques), atomic force microscopy, andradio frequency methods, e.g., multipolar resonance spectroscopy.Illustrative of optical methods, in addition to microscopy, bothconfocal and non-confocal, are detection of fluorescence, luminescence,chemiluminescence, absorbance, reflectance, transmittance, andbirefringence or refractive index (e.g., surface plasmon resonance,ellipsometry, a resonant mirror method, a grating coupler waveguidemethod or interferometry).

In one embodiment, a sample is analyzed by means of a biochip. A biochipgenerally comprises a solid substrate having a substantially planarsurface, to which a capture reagent (also called an adsorbent oraffinity reagent) is attached. Frequently, the surface of a biochipcomprises a plurality of addressable locations, each of which has thecapture reagent bound there.

Protein biochips are biochips adapted for the capture of polypeptides.Many protein biochips are described in the art. These include, forexample, protein biochips produced by Ciphergen Biosystems, Inc.(Fremont, Calif.), Zyomyx (Hayward, Calif.), Invitrogen (Carlsbad,Calif.), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK).Examples of such protein biochips are described in the following patentsor published patent applications: U.S. Pat. No. 6,225,047 (Hutchens &Yip); U.S. Pat. No. 6,537,749 (Kuimelis and Wagner); U.S. Pat. No.6,329,209 (Wagner et al.); PCT International Publication No. WO 00/56934(Englert et al.); PCT International Publication No. WO 03/048768(Boutell et al.) and U.S. Pat. No. 5,242,828 (Bergstrom et al.).

4.1. Detection by Mass Spectrometry

In a preferred embodiment, the biomarkers of this invention are detectedby mass spectrometry, a method that employs a mass spectrometer todetect gas phase ions. Examples of mass spectrometers aretime-of-flight, magnetic sector, quadrupole filter, ion trap, ioncyclotron resonance, electrostatic sector analyzer and hybrids of these.

In a further preferred method, the mass spectrometer is a laserdesorption/ionization mass spectrometer. In laser desorption/ionizationmass spectrometry, the analytes are placed on the surface of a massspectrometry probe, a device adapted to engage a probe interface of themass spectrometer and to present an analyte to ionizing energy forionization and introduction into a mass spectrometer. A laser desorptionmass spectrometer employs laser energy, typically from an ultravioletlaser, but also from an infrared laser, to desorb analytes from asurface, to volatilize and ionize them and make them available to theion optics of the mass spectrometer. The analysis of proteins by LDI cantake the form of MALDI or of SELDI

4.1.1. SELDI

A preferred mass spectrometric technique for use in the invention is“Surface Enhanced Laser Desorption and Ionization” or “SELDI,” asdescribed, for example, in U.S. Pat. No. 5,719,060 and U.S. Pat. No.6,225,047, both to Hutchens and Yip. This refers to a method ofdesorption/ionization gas phase ion spectrometry (e.g., massspectrometry) in which an analyte (here, one or more of the biomarkers)is captured on the surface of a SELDI mass spectrometry probe.

SELDI also has been called is called “affinity capture massspectrometry” or “Surface-Enhanced Affinity Capture” (“SEAC”). Thisversion involves the use of probes that have a material on the probesurface that captures analytes through a non-covalent affinityinteraction (adsorption) between the material and the analyte. Thematerial is variously called an “adsorbent,” a “capture reagent,” an“affinity reagent” or a “binding moiety.” Such probes can be referred toas “affinity capture probes” and as having an “adsorbent surface.” Thecapture reagent can be any material capable of binding an analyte. Thecapture reagent is attached to the probe surface by physisorption orchemisorption. In certain embodiments the probes have the capturereagent already attached to the surface. In other embodiments, theprobes are pre-activated and include a reactive moiety that is capableof binding the capture reagent, e.g., through a reaction forming acovalent or coordinate covalent bond. Epoxide and acyl-imidizole areuseful reactive moieties to covalently bind polypeptide capture reagentssuch as antibodies or cellular receptors. Nitrilotriacetic acid andiminodiacetic acid are useful reactive moieties that function aschelating agents to bind metal ions that interact non-covalently withhistidine containing peptides. Adsorbents are generally classified aschromatographic adsorbents and biospecific adsorbents.

“Chromatographic adsorbent” refers to an adsorbent material typicallyused in chromatography. Chromatographic adsorbents include, for example,ion exchange materials, metal chelators (e.g., nitrilotriacetic acid oriminodiacetic acid), immobilized metal chelates, hydrophobic interactionadsorbents, hydrophilic interaction adsorbents, dyes, simplebiomolecules (e.g., nucleotides, amino acids, simple sugars and fattyacids) and mixed mode adsorbents (e.g., hydrophobicattraction/electrostatic repulsion adsorbents).

“Biospecific adsorbent” refers to an adsorbent comprising a biomolecule,e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, apolysaccharide, a lipid, a steroid or a conjugate of these (e.g., aglycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g.,DNA)-protein conjugate). In certain instances, the biospecific adsorbentcan be a macromolecular structure such as a multiprotein complex, abiological membrane or a virus. Examples of biospecific adsorbents areantibodies, receptor proteins and nucleic acids. Biospecific adsorbentstypically have higher specificity for a target analyte thanchromatographic adsorbents. Further examples of adsorbents for use inSELDI can be found in U.S. Pat. No. 6,225,047. A “bioselectiveadsorbent” refers to an adsorbent that binds to an analyte with anaffinity of at least 10⁻⁸ M.

Protein biochips produced by Ciphergen Biosystems, Inc. comprisesurfaces having chromatographic or biospecific adsorbents attachedthereto at addressable locations. Ciphergen ProteinChip® arrays includeNP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q-10 and (anionexchange); WCX-2 and CM-10 (cation exchange); IMAC-3, IMAC-30 andIMAC-50 (metal chelate); and PS-10, PS-20 (reactive surface withacyl-imidizole, epoxide) and PG-20 (protein G coupled throughacyl-imidizole). Hydrophobic ProteinChip arrays have isopropyl ornonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anionexchange ProteinChip arrays have quaternary ammonium functionalities.Cation exchange ProteinChip arrays have carboxylate functionalities.Immobilized metal chelate ProteinChip arrays have nitrilotriacetic acidfunctionalities (IMAC 3 and IMAC 30) orO-methacryloyl-N,N-bis-carboxymethyl tyrosine funtionalities (IMAC 50)that adsorb transition metal ions, such as copper, nickel, zinc, andgallium, by chelation. Preactivated ProteinChip arrays haveacyl-imidizole or epoxide functional groups that can react with groupson proteins for covalent binding.

Such biochips are further described in: U.S. Pat. No. 6,579,719(Hutchens and Yip, “Retentate Chromatography,” Jun. 17, 2003); U.S. Pat.No. 6,897,072 (Rich et al., “Probes for a Gas Phase Ion Spectrometer,”May 24, 2005); U.S. Pat. No. 6,555,813 (Beecher et al., “Sample Holderwith Hydrophobic Coating for Gas Phase Mass Spectrometer,” Apr. 29,2003); U.S. Patent Publication No. U.S. 2003-0032043 A1 (Pohl andPapanu, “Latex Based Adsorbent Chip,” Jul. 16, 2002); and PCTInternational Publication No. WO 03/040700 (Urn et al., “HydrophobicSurface Chip,” May 15, 2003); U.S. Patent Publication No. US2003-0218130 A1 (Boschetti et al., “Biochips With Surfaces Coated WithPolysaccharide-Based Hydrogels,” Apr. 14, 2003) and U.S. PatentPublication No. U.S. 2005-059086 A1 (Huang et al., “PhotocrosslinkedHydrogel Blend Surface Coatings,” Mar. 17, 2005).

In general, a probe with an adsorbent surface is contacted with thesample for a period of time sufficient to allow the biomarker orbiomarkers that may be present in the sample to bind to the adsorbent.After an incubation period, the substrate is washed to remove unboundmaterial. Any suitable washing solutions can be used; preferably,aqueous solutions are employed. The extent to which molecules remainbound can be manipulated by adjusting the stringency of the wash. Theelution characteristics of a wash solution can depend, for example, onpH, ionic strength, hydrophobicity, degree of chaotropism, detergentstrength, and temperature. Unless the probe has both SEAC and SENDproperties (as described herein), an energy absorbing molecule then isapplied to the substrate with the bound biomarkers.

In yet another method, one can capture the biomarkers with a solid-phasebound immuno-adsorbent that has antibodies that bind the biomarkers.After washing the adsorbent to remove unbound material, the biomarkersare eluted from the solid phase and detected by applying to a SELDI chipthat binds the biomarkers and analyzing by SELDI.

The biomarkers bound to the substrates are detected in a gas phase ionspectrometer such as a time-of-flight mass spectrometer. The biomarkersare ionized by an ionization source such as a laser, the generated ionsare collected by an ion optic assembly, and then a mass analyzerdisperses and analyzes the passing ions. The detector then translatesinformation of the detected ions into mass-to-charge ratios. Detectionof a biomarker typically will involve detection of signal intensity.Thus, both the quantity and mass of the biomarker can be determined.

4.1.2. SEND

Another method of laser desorption mass spectrometry is calledSurface-Enhanced Neat Desorption (“SEND”). SEND involves the use ofprobes comprising energy absorbing molecules that are chemically boundto the probe surface (“SEND probe”). The phrase “energy absorbingmolecules” (EAM) denotes molecules that are capable of absorbing energyfrom a laser desorption/ionization source and, thereafter, contribute todesorption and ionization of analyte molecules in contact therewith. TheEAM category includes molecules used in MALDI, frequently referred to as“matrix,” and is exemplified by cinnamic acid derivatives, sinapinicacid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoicacid, ferulic acid, and hydroxyaceto-phenone derivatives. In certainembodiments, the energy absorbing molecule is incorporated into a linearor cross-linked polymer, e.g., a polymethacrylate. For example, thecomposition can be a co-polymer of α-cyano-4-methacryloyloxycinnamicacid and acrylate. In another embodiment, the composition is aco-polymer of α-cyano-4-methacryloyloxycinnamic acid, acrylate and3-(triethoxy)silyl propyl methacrylate. In another embodiment, thecomposition is a co-polymer of α-cyano-4-methacryloyloxycinnamic acidand octadecylmethacrylate (“C18 SEND”). SEND is further described inU.S. Pat. No. 6,124,137 and PCT International Publication No. WO03/64594 (Kitagawa, “Monomers And Polymers Having Energy AbsorbingMoieties Of Use In Desorption/Ionization Of Analytes,” Aug. 7, 2003).

SEAC/SEND is a version of laser desorption mass spectrometry in whichboth a capture reagent and an energy absorbing molecule are attached tothe sample presenting surface. SEAC/SEND probes therefore allow thecapture of analytes through affinity capture and ionization/desorptionwithout the need to apply external matrix. The C18 SEND biochip is aversion of SEAC/SEND, comprising a C18 moiety which functions as acapture reagent, and a CHCA moiety which functions as an energyabsorbing moiety.

4.1.3. SEPAR

Another version of LDI is called Surface-Enhanced Photolabile Attachmentand Release (“SEPAR”). SEPAR involves the use of probes having moietiesattached to the surface that can covalently bind an analyte, and thenrelease the analyte through breaking a photolabile bond in the moietyafter exposure to light, e.g., to laser light (see, U.S. Pat. No.5,719,060). SEPAR and other forms of SELDI are readily adapted todetecting a biomarker or biomarker profile, pursuant to the presentinvention.

4.1.4. MALDI

MALDI is a traditional method of laser desorption/ionization used toanalyte biomolecules such as proteins and nucleic acids. In one MALDImethod, the sample is mixed with matrix and deposited directly on aMALDI chip. However, the complexity of biological samples such as serumor urine make this method less than optimal without prior fractionationof the sample. Accordingly, in certain embodiments with biomarkers arepreferably first captured with biospecific (e.g., an antibody) orchromatographic materials coupled to a solid support such as a resin(e.g., in a spin column). Specific affinity materials that bind thebiomarkers of this invention are described above. After purification onthe affinity material, the biomarkers are eluted and then detected byMALDI.

4.1.5. Other Forms of Ionization in Mass Spectrometry

In another method, the biomarkers are detected by LC-MS or LC-LC-MS.This involves resolving the proteins in a sample by one or two passesthrough liquid chromatography, followed by mass spectrometry analysis,typically electrospray ionization.

4.1.6. Data Analysis

Analysis of analytes by time-of-flight mass spectrometry generates atime-of-flight spectrum. The time-of-flight spectrum ultimately analyzedtypically does not represent the signal from a single pulse of ionizingenergy against a sample, but rather the sum of signals from a number ofpulses. This reduces noise and increases dynamic range. Thistime-of-flight data is then subject to data processing. In Ciphergen'sProteinChip® software, data processing typically includes TOF-to-M/Ztransformation to generate a mass spectrum, baseline subtraction toeliminate instrument offsets and high frequency noise filtering toreduce high frequency noise.

Data generated by desorption and detection of biomarkers can be analyzedwith the use of a programmable digital computer. The computer programanalyzes the data to indicate the number of biomarkers detected, andoptionally the strength of the signal and the determined molecular massfor each biomarker detected. Data analysis can include steps ofdetermining signal strength of a biomarker and removing data deviatingfrom a predetermined statistical distribution. For example, the observedpeaks can be normalized, by calculating the height of each peak relativeto some reference.

The computer can transform the resulting data into various formats fordisplay. The standard spectrum can be displayed, but in one usefulformat only the peak height and mass information are retained from thespectrum view, yielding a cleaner image and enabling biomarkers withnearly identical molecular weights to be more easily seen. In anotheruseful format, two or more spectra are compared, convenientlyhighlighting unique biomarkers and biomarkers that are up- ordown-regulated between samples. Using any of these formats, one canreadily determine whether a particular biomarker is present in a sample.

Analysis generally involves the identification of peaks in the spectrumthat represent signal from an analyte. Peak selection can be donevisually, but software is available, as part of Ciphergen's ProteinChip®software package, that can automate the detection of peaks. In general,this software functions by identifying signals having a signal-to-noiseratio above a selected threshold and labeling the mass of the peak atthe centroid of the peak signal. In one useful application, many spectraare compared to identify identical peaks present in some selectedpercentage of the mass spectra. One version of this software clustersall peaks appearing in the various spectra within a defined mass range,and assigns a mass (M/Z) to all the peaks that are near the mid-point ofthe mass (M/Z) cluster.

Software used to analyze the data can include code that applies analgorithm to the analysis of the signal to determine whether the signalrepresents a peak in a signal that corresponds to a biomarker accordingto the present invention. The software also can subject the dataregarding observed biomarker peaks to classification tree or ANNanalysis, to determine whether a biomarker peak or combination ofbiomarker peaks is present that indicates the status of the particularclinical parameter under examination. Analysis of the data may be“keyed” to a variety of parameters that are obtained, either directly orindirectly, from the mass spectrometric analysis of the sample. Theseparameters include, but are not limited to, the presence or absence ofone or more peaks, the shape of a peak or group of peaks, the height ofone or more peaks, the log of the height of one or more peaks, and otherarithmetic manipulations of peak height data.

4.1.7. General Protocol for SELDI Detection of Biomarkers for OvarianCancer

A preferred protocol for the detection of the biomarkers of thisinvention is as follows. The biological sample to be tested, e.g.,serum, preferably is subject to pre-fractionation before SELDI analysis.This simplifies the sample and improves sensitivity. A preferred methodof pre-fractionation involves contacting the sample with an anionexchange chromatographic material, such as Q HyperD (BioSepra, SA). Thebound materials are then subject to stepwise pH elution using buffers atpH 9, pH 7, pH 5 and pH 4. (The fractions in which the biomarkers areeluted also is indicated in Table 1.) Various fractions containing thebiomarker are collected.

The sample to be tested (preferably pre-fractionated) is then contactedwith an affinity capture probe comprising an cation exchange adsorbent(preferably a CM10 ProteinChip array (Ciphergen Biosystems, Inc.)) or anIMAC adsorbent (preferably an IMAC30 ProteinChip array (CiphergenBiosystems, Inc.)), again as indicated in Table 1. The probe is washedwith a buffer that will retain the biomarker while washing away unboundmolecules. A suitable wash for each biomarker is the buffer identifiedin Table 1. The biomarkers are detected by laser desorption/ionizationmass spectrometry.

Alternatively, samples may be diluted, with or without denaturing, inthe appropriate array binding buffer and bound and washed underconditions optimized for detecting each analyte.

ApoA1 Chromatographic Assay Performed on Tecan Aquarius-96:

-   -   1. Denature serum: add 7.5 ul 9M urea 2% CHAPS 50 mM Tris HCl        pH9 to 5 ul of human serum, and mix at room temperature for 20        min. Dilute 1:400 with a solution containing: H50 buffer (10%        acetonitrile, 0.1% TFA), and 0.12 mg/ml E. coli lysate.    -   2. Pre-activate H50 arrays: wash each well of the BioProcessor        with 50% acetonitrile per well. Incubate at room temp for 5 min.        Remove solution. Equilibrate with 150 μl H50 wash buffer (10%        acetonitrile/0.1% TFA) two times for 5 minutes each. Remove        buffer.    -   3. Add 50 ul of 1:400 diluted serum sample to each well.        Incubate at room temp for 120 min.    -   4. Wash arrays 4 times with 150 ul of H50 buffer. Wash arrays        with 150 ul of water 1 time.    -   5. Remove BioProcessor top. Air dry for 10 minutes.    -   6. Using a BioDot, add 0.75 ul of sinapinic acid matrix (SPA,        Ciphergen, 12.5 mg/ml in 50% acetonitrile/0.5% TFA/0.1% TX100)        per spot. Air day for 10 min in the BioDot chamber. Apply        additional 0.75 ul SPA solution per spot. Air dry for 30 min in        the chamber before reading arrays on PCS4000.    -   7. Read on PCS 4000, with focus mass at 28,000 Da, collect 10        shots per partition for a total of 530 shots per spot.

Transthyretin Chromatographic Assay Performed on Tecan Aquarius-96:

-   -   1. Sample dilution: 1:250 dilution of serum sample in a solution        containing: 100 mM Sodium Phosphate buffer, pH 7.0 (PB buffer)        with addition of 0.05 mg/ml E. coli lysate. Mix well.    -   2. Pre-treat Q10 arrays with the PB buffer, incubate 5 minutes.        Remove buffer. Repeat once.    -   3. Add 50 ul of 1:250 diluted serum sample to each well and        incubate for 120 min at room temp. Remove samples.    -   4. Wash arrays 4 times with 150 ul of PB buffer. Remove buffer        after each wash.    -   5. Wash arrays with 150 ul of water one time. Remove water.    -   6. Remove BioProcessor. Air dry arrays for 30 minutes.    -   7. Using a BioDot, add 0.75 ul of sinapinic acid matrix (SPA,        Ciphergen, 12.5 mg/ml in 50% acetonitrile/0.5% TFA) per spot.        Air day for 10 min in the BioDot chamber. Apply additional 0.75        ul SPA solution per spot. Air dry for 30 min in the chamber        before reading arrays on PCS4000.    -   8. Read on PCS 4000, with focus mass at 14,000 Da, collect 10        shots per partition for a total of 530 shots per spot.

ITIH4 Chromatographic Assay Performed on Tecan Aquarius-96:

-   -   1. Sample dilution: 1:50 dilution of serum sample in IMAC        binding/washing buffer (50 mM Na phosphate 0.25M NaCl pH 6.0).        Mix well.    -   2. Pre-activate IMAC50 arrays: add 50 mM CuSO4 per well in a        BioProcessor. Incubate at room temp for 10 min. Remove copper        solution. Wash with water 4 times. Equilibrate IMAC50 arrays        twice with the binding buffer.    -   3. Add 50 ul of 1:50 diluted serum sample to each well and        incubate for 120 min at room temp. Remove samples.    -   4. Wash arrays 3 times with 150 ul of IMAC binding/washing        buffer. Remove buffer after each wash.    -   5. Wash arrays with 150 ul of water 2 times. Remove water.    -   6. Remove BioProcessor. Air dry arrays for 30 minutes.    -   7. Using a BioDot, add 0.75 ul of sinapinic acid matrix (SPA,        Ciphergen, 12.5 mg/ml in 50% acetonitrile/0.5% TFA) per spot.        Air day for 10 min in the BioDot chamber. Apply additional 0.75        ul SPA solution per spot. Air dry for 30 min in the chamber        before reading arrays on PCS4000.    -   8. Read on PCS 4000, with focus mass at 3,273 Da, collect 10        shots per partition for a total of 530 shots per spot.

Alternatively, if antibodies that recognize the biomarker are available,for example from Dako, U.S. Biological, Chemicon, Abcam and Genway.These can be attached to the surface of a probe, such as a pre-activatedPS10 or PS20 ProteinChip array (Ciphergen Biosystems, Inc.). Theseantibodies can capture the biomarkers from a sample onto the probesurface. Then the biomarkers can be detected by, e.g., laserdesorption/ionization mass spectrometry.

Any robot that performs fluidics operations can be used in these assays,for example, those available from Hewlett Packard and Hamilton.

4.2. Detection by Immunoassay

In another embodiment of the invention, the biomarkers of the inventionare measured by a method other than mass spectrometry or other thanmethods that rely on a measurement of the mass of the biomarker. In onesuch embodiment that does not rely on mass, the biomarkers of thisinvention are measured by immunoassay. Immunoassay requires biospecificcapture reagents, such as antibodies, to capture the biomarkers.Antibodies can be produced by methods well known in the art, e.g., byimmunizing animals with the biomarkers. Biomarkers can be isolated fromsamples based on their binding characteristics. Alternatively, if theamino acid sequence of a polypeptide biomarker is known, the polypeptidecan be synthesized and used to generate antibodies by methods well knownin the art.

This invention contemplates traditional immunoassays including, forexample, sandwich immunoassays including ELISA or fluorescence-basedimmunoassays, as well as other enzyme immunoassays. Nephelometry is anassay done in liquid phase, in which antibodies are in solution. Bindingof the antigen to the antibody results in changes in absorbance, whichis measured. In the SELDI-based immunoassay, a biospecific capturereagent for the biomarker is attached to the surface of an MS probe,such as a pre-activated ProteinChip array. The biomarker is thenspecifically captured on the biochip through this reagent, and thecaptured biomarker is detected by mass spectrometry.

5. Determination of Subject Ovarian Cancer Status

The biomarkers of the invention can be used in diagnostic tests toassess ovarian cancer status in a subject, e.g., to diagnose ovariancancer. The phrase “ovarian cancer status” includes any distinguishablemanifestation of the disease, including non-disease. For example,ovarian cancer status includes, without limitation, the presence orabsence of disease (e.g., ovarian cancer v. non-ovarian cancer), therisk of developing disease, the stage of the disease, the progression ofdisease (e.g., progress of disease or remission of disease over time)and the effectiveness or response to treatment of disease.

The correlation of test results with ovarian cancer status involvesapplying a classification algorithm of some kind to the results togenerate the status. The classification algorithm may be as simple asdetermining whether or not the amount of hepcidin measured is above orbelow a particular cut-off number. When multiple biomarkers are used,the classification algorithm may be a linear regression formula.Alternatively, the classification algorithm may be the product of any ofa number of learning algorithms described herein.

In the case of complex classification algorithms, it may be necessary toperform the algorithm on the data, thereby determining theclassification, using a computer, e.g., a programmable digital computer.In either case, one can then record the status on tangible medium, forexample, in computer-readable format such as a memory drive or disk orsimply printed on paper. The result also could be reported on a computerscreen.

5.1. Single Markers

The power of a diagnostic test to correctly predict status is commonlymeasured as the sensitivity of the assay, the specificity of the assayor the area under a receiver operated characteristic (“ROC”) curve.Sensitivity is the percentage of true positives that are predicted by atest to be positive, while specificity is the percentage of truenegatives that are predicted by a test to be negative. An ROC curveprovides the sensitivity of a test as a function of 1-specificity. Thegreater the area under the ROC curve, the more powerful the predictivevalue of the test. Other useful measures of the utility of a test arepositive predictive value and negative predictive value. Positivepredictive value is the percentage of people who test positive that areactually positive. Negative predictive value is the percentage of peoplewho test negative that are actually negative.

The biomarkers of this invention show a statistical difference indifferent ovarian cancer statuses. Diagnostic tests that use thesebiomarkers alone or in combination show a sensitivity and specificity ofat least 75%, at least 80%, at least 85%, at least 90%, at least 95%, atleast 98% and about 100%.

Each biomarker listed in Table 1 is differentially present in ovariancancer, and, therefore, each is individually useful in aiding in thedetermination of ovarian cancer status. The method involves, first,measuring the selected biomarker in a subject sample using the methodsdescribed herein, e.g., capture on a SELDI biochip followed by detectionby mass spectrometry and, second, comparing the measurement with adiagnostic amount or cut-off that distinguishes a positive ovariancancer status from a negative ovarian cancer status. The diagnosticamount represents a measured amount of a biomarker above which or belowwhich a subject is classified as having a particular ovarian cancerstatus. For example, because hepcidin is up-regulated in ovarian cancercompared to normal, then a measured amount of hepcidin above thediagnostic cutoff provides a diagnosis of ovarian cancer. As is wellunderstood in the art, by adjusting the particular diagnostic cut-offused in an assay, one can increase sensitivity or specificity of thediagnostic assay depending on the preference of the diagnostician. Theparticular diagnostic cut-off can be determined, for example, bymeasuring the amount of the biomarker in a statistically significantnumber of samples from subjects with the different ovarian cancerstatuses, as was done here, and drawing the cut-off to suit thediagnostician's desired levels of specificity and sensitivity.

5.2. Combinations of Markers

While individual biomarkers are useful diagnostic biomarkers, it hasbeen found that a combination of biomarkers can provide greaterpredictive value of a particular status than single biomarkers alone.Specifically, the detection of a plurality of biomarkers in a sample canincrease the sensitivity and/or specificity of the test. A combinationof at least two biomarkers is sometimes referred to as a “biomarkerprofile” or “biomarker fingerprint.” Accordingly, hepcidin can becombined with other biomarkers for ovarian or endometrial cancer toimprove the sensitivity and/or specificity of the diagnostic test.

In particular, it has been found that a diagnostic test for ovariancancer status involving the measurement of both hepcidin andtransthyretin has greater predictive power than the measurement ofhepcidin alone. As indicated, hepcidin levels are increased in ovariancancer and transthyretin levels are decreased. It further has been foundthat a diagnostic test combining at least three biomarkers or, incertain instances, seven biomarkers, provides greater predictive powerthan the measurement of both hepcidin and transthyretin. Morespecifically, it is contemplated that a diagnostic test for ovariancancer status will include measuring hepcidin, transthyretin and atleast one of Apo A1, transferrin, CTAP-III and ITIH4 fragment, andcorrelating these measurements with ovarian cancer status. It is alsocontemplated that β2-microglobulin could be combined with hepcidin andtransthyretin, along with any of the four aforementioned biomarkers.

In a study on samples of a Japanese cohort, the combination of hepcidin,ApoA1, β2 microglobulin and CTAP-III was found to be a particularlyeffective diagnostic combination.

The diagnosis of ovarian cancer typically involves the measurement ofCA125, as increased levels of this marker are correlated with ovariancancer. Therefore, levels of CA125 can be correlated with anycombination of the above markers in determining ovarian cancer status.

Other biomarkers with which hepcidin can be combined include, but arenot limited to, CA125, CA125 II, CA15-3, CA19-9, CA72-4, CA 195, tumorassociated trypsin inhibitor (TATI), CEA, placental alkaline phosphatase(PLAP), Sialyl TN, galactosyltransferase, macrophage colony stimulatingfactor (M-CSF, CSF-1), lysophosphatidic acid (LPA), 110 kD component ofthe extracellular domain of the epidermal growth factor receptor(p110EGFR), tissue kallikreins, e.g., kallikrein 6 and kallikrein 10(NES-1), prostasin, HE4, creatine kinase B (CKB), LASA, HER-2/neu,urinary gonadotropin peptide, Dianon NB 70/K, Tissue peptide antigen(TPA), osteopontin, and haptoglobin, leptin, prolactin, insulin likegrowth factor I or II. CA125 is especially useful in that womenundergoing tests for ovarian cancer typically have CA125 tested asroutine part of the work-up.

5.3. Ovarian Cancer Status

Determining ovarian cancer status typically involves classifying anindividual into one of two or more groups (statuses) based on theresults of the diagnostic test. The diagnostic tests described hereincan be used to classify between a number of different states.

5.3.1. Presence of Disease

In one embodiment, this invention provides methods for determining thepresence or absence of ovarian cancer in a subject (status: ovariancancer v. non-ovarian cancer). The presence or absence of ovarian canceris determined by measuring the relevant biomarker or biomarkers and theneither submitting them to a classification algorithm or comparing themwith a reference amount and/or pattern of biomarkers that is associatedwith the particular risk level.

5.3.2. Determining Risk of Developing Disease

In one embodiment, this invention provides methods for determining therisk of developing ovarian cancer in a subject (status: low-risk v. highrisk). Biomarker amounts or patterns are characteristic of various riskstates, e.g., high, medium or low. The risk of developing a disease isdetermined by measuring the relevant biomarker or biomarkers and theneither submitting them to a classification algorithm or comparing themwith a reference amount and/or pattern of biomarkers that is associatedwith the particular risk level

5.3.3. Determining Stage of Disease

In one embodiment, this invention provides methods for determining thestage of disease in a subject. Each stage of the disease has acharacteristic amount of a biomarker or relative amounts of a set ofbiomarkers (a pattern). The stage of a disease is determined bymeasuring the relevant biomarker or biomarkers and then eithersubmitting them to a classification algorithm or comparing them with areference amount and/or pattern of biomarkers that is associated withthe particular stage. For example, one can classify between early stageovarian cancer and non-ovarian cancer or among stage I ovarian cancer,stage II ovarian cancer and stage III ovarian cancer.

5.3.4. Determining Course (Progression/Remission) of Disease

In one embodiment, this invention provides methods for determining thecourse of disease in a subject. Disease course refers to changes indisease status over time, including disease progression (worsening) anddisease regression (improvement). Over time, the amounts or relativeamounts (e.g., the pattern) of the biomarkers changes. For example,hepcidin is increased with disease, while transthryetin is decreased indisease. Therefore, the trend of these markers, either increased ordecreased over time toward diseased or non-diseased indicates the courseof the disease. Accordingly, this method involves measuring one or morebiomarkers in a subject for at least two different time points, e.g., afirst time and a second time, and comparing the change in amounts, ifany. The course of disease is determined based on these comparisons.

5.4. Reporting the Status

Additional embodiments of the invention relate to the communication ofassay results or diagnoses or both to technicians, physicians orpatients, for example. In certain embodiments, computers will be used tocommunicate assay results or diagnoses or both to interested parties,e.g., physicians and their patients. In some embodiments, the assayswill be performed or the assay results analyzed in a country orjurisdiction which differs from the country or jurisdiction to which theresults or diagnoses are communicated.

In a preferred embodiment of the invention, a diagnosis based on thedifferential presence in a test subject of any the biomarkers of Table 1is communicated to the subject as soon as possible after the diagnosisis obtained. The diagnosis may be communicated to the subject by thesubject's treating physician. Alternatively, the diagnosis may be sentto a test subject by email or communicated to the subject by phone. Acomputer may be used to communicate the diagnosis by email or phone. Incertain embodiments, the message containing results of a diagnostic testmay be generated and delivered automatically to the subject using acombination of computer hardware and software which will be familiar toartisans skilled in telecommunications. One example of ahealthcare-oriented communications system is described in U.S. Pat. No.6,283,761; however, the present invention is not limited to methodswhich utilize this particular communications system. In certainembodiments of the methods of the invention, all or some of the methodsteps, including the assaying of samples, diagnosing of diseases, andcommunicating of assay results or diagnoses, may be carried out indiverse (e.g., foreign) jurisdictions.

5.5. Subject Management

In certain embodiments of the methods of qualifying ovarian cancerstatus, the methods further comprise managing subject treatment based onthe status. Such management includes the actions of the physician orclinician subsequent to determining ovarian cancer status. For example,if a physician makes a diagnosis of ovarian cancer, then a certainregime of treatment, such as prescription or administration ofchemotherapy might follow. Alternatively, a diagnosis of non-ovariancancer or non-ovarian cancer might be followed with further testing todetermine a specific disease that might the patient might be sufferingfrom. Also, if the diagnostic test gives an inconclusive result onovarian cancer status, further tests may be called for.

6. Generation of Classification Algorithms for Qualifying Ovarian CancerStatus

In some embodiments, data derived from the spectra (e.g., mass spectraor time-of-flight spectra) that are generated using samples such as“known samples” can then be used to “train” a classification model. A“known sample” is a sample that has been pre-classified. The data thatare derived from the spectra and are used to form the classificationmodel can be referred to as a “training data set.” Once trained, theclassification model can recognize patterns in data derived from spectragenerated using unknown samples. The classification model can then beused to classify the unknown samples into classes. This can be useful,for example, in predicting whether or not a particular biological sampleis associated with a certain biological condition (e.g., diseased versusnon-diseased).

The training data set that is used to form the classification model maycomprise raw data or pre-processed data. In some embodiments, raw datacan be obtained directly from time-of-flight spectra or mass spectra,and then may be optionally “pre-processed” as described above.

Classification models can be formed using any suitable statisticalclassification (or “learning”) method that attempts to segregate bodiesof data into classes based on objective parameters present in the data.Classification methods may be either supervised or unsupervised.Examples of supervised and unsupervised classification processes aredescribed in Jain, “Statistical Pattern Recognition: A Review”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.1, January 2000, the teachings of which are incorporated by reference.

In supervised classification, training data containing examples of knowncategories are presented to a learning mechanism, which learns one ormore sets of relationships that define each of the known classes. Newdata may then be applied to the learning mechanism, which thenclassifies the new data using the learned relationships. Examples ofsupervised classification processes include linear regression processes(e.g., multiple linear regression (MLR), partial least squares (PLS)regression and principal components regression (PCR)), binary decisiontrees (e.g., recursive partitioning processes such asCART—classification and regression trees), artificial neural networkssuch as back propagation networks, discriminant analyses (e.g., Bayesianclassifier or Fischer analysis), logistic classifiers, and supportvector classifiers (support vector machines).

A preferred supervised classification method is a recursive partitioningprocess. Recursive partitioning processes use recursive partitioningtrees to classify spectra derived from unknown samples. Further detailsabout recursive partitioning processes are provided in U.S. Pat. No.6,675,104 (Paulse et al., “Method for analyzing mass spectra”).

In other embodiments, the classification models that are created can beformed using unsupervised learning methods. Unsupervised classificationattempts to learn classifications based on similarities in the trainingdata set, without pre-classifying the spectra from which the trainingdata set was derived. Unsupervised learning methods include clusteranalyses. A cluster analysis attempts to divide the data into “clusters”or groups that ideally should have members that are very similar to eachother, and very dissimilar to members of other clusters. Similarity isthen measured using some distance metric, which measures the distancebetween data items, and clusters together data items that are closer toeach other. Clustering techniques include the MacQueen's K-meansalgorithm and the Kohonen's Self-Organizing Map algorithm.

Learning algorithms asserted for use in classifying biologicalinformation are described, for example, in PCT International PublicationNo. WO 01/31580 (Barnhill et al., “Methods and devices for identifyingpatterns in biological systems and methods of use thereof”), U.S. PatentApplication No. 2002 0193950 A1 (Gavin et al., “Method or analyzing massspectra”), U.S. Patent Application No. 2003 0004402 A1 (Hitt et al.,“Process for discriminating between biological states based on hiddenpatterns from biological data”), and U.S. Patent Application No. 20030055615 A1 (Zhang and Zhang, “Systems and methods for processingbiological expression data”).

The classification models can be formed on and used on any suitabledigital computer. Suitable digital computers include micro, mini, orlarge computers using any standard or specialized operating system, suchas a Unix, Windows™ or Linux™ based operating system. The digitalcomputer that is used may be physically separate from the massspectrometer that is used to create the spectra of interest, or it maybe coupled to the mass spectrometer.

The training data set and the classification models according toembodiments of the invention can be embodied by computer code that isexecuted or used by a digital computer. The computer code can be storedon any suitable computer readable media including optical or magneticdisks, sticks, tapes, etc., and can be written in any suitable computerprogramming language including C, C++, visual basic, etc.

The learning algorithms described above are useful both for developingclassification algorithms for the biomarkers already discovered, or forfinding new biomarkers for ovarian cancer. The classificationalgorithms, in turn, form the base for diagnostic tests by providingdiagnostic values (e.g.; cut-off points) for biomarkers used singly orin combination.

A logistical regression analysis was performed on data generated fromthe experiments described in Example 4, below (smaller sample set). Theanalysis generated a classification algorithm to distinguish ovariancancer from non-ovarian cancer based on seven biomarkers: hepcidin,ITIH4 fragment 1, CTAP-III, transthyretin, transferrin, beta-2microglobulin and Apo-A1. The algorithm involved two steps. In the firststep a number was generated from a test sample based on the followingformula:

Logit=−1.673+0.7349*hepc−0.6252*ITIH4conc+0.1458*CTAP-III2−0.4923*Ttconc−0.5023*TFR−0.1595*M2B+0.0265*ApoA1conc

The measurements represented either normalized peak intensity or analyteconcentration (designated “conc”). In the second step, the probabilitythat a sample came from a subject having ovarian cancer was determinedby the formula: e^(Logit)/(1+e^(Logit)). A cut-off is then applied basedon the desired sensitivity or specificity of the test. The higher thecut-off number, the better the sensitivity of the assay. The specificnumbers used in this assay depend upon the assay conditions andinstrument used, and need to be re-calibrated whenever an assay is setup.

7. Compositions of Matter

In another aspect, this invention provides compositions of matter basedon the biomarkers of this invention.

In one embodiment, this invention provides biomarkers of this inventionin purified form. Purified biomarkers have utility as antigens to raiseantibodies. Purified biomarkers also have utility as standards in assayprocedures. As used herein, a “purified biomarker” is a biomarker thathas been isolated from other proteins and peptides, and/or othermaterial from the biological sample in which the biomarker is found. Thebiomarkers can be isolated from biological fluids, such as urine orserum. Biomarkers may be purified using any method known in the art,including, but not limited to, mechanical separation (e.g.,centrifugation), ammonium sulphate precipitation, dialysis (includingsize-exclusion dialysis), electrophoresis (e.g. acrylamide gelelectrophoresis) size-exclusion chromatography, affinity chromatography,anion-exchange chromatography, cation-exchange chromatography, andmethal-chelate chromatography. Such methods may be performed at anyappropriate scale, for example, in a chromatography column, or on abiochip.

In another embodiment, this invention provides a biospecific capturereagent, optionally in purified form, that specifically binds abiomarker of this invention. In one embodiment, the biospecific capturereagent is an antibody. Such compositions are useful for detecting thebiomarker in a detection assay, e.g., for diagnostics.

In another embodiment, this invention provides an article comprising abiospecific capture reagent that binds a biomarker of this invention,wherein the reagent is bound to a solid phase. For example, thisinvention contemplates a device comprising bead, chip, membrane,monolith or microtiter plate derivatized with the biospecific capturereagent. Such articles are useful in biomarker detection assays.

In another aspect this invention provides a composition comprising abiospecific capture reagent, such as an antibody, bound to a biomarkerof this invention, the composition optionally being in purified form.Such compositions are useful for purifying the biomarker or in assaysfor detecting the biomarker.

In another embodiment, this invention provides an article comprising asolid substrate to which is attached an adsorbent, e.g., achromatographic adsorbent or a biospecific capture reagent, to which isfurther bound a biomarker of this invention. In one embodiment, thearticle is a biochip or a probe for mass spectrometry, e.g., a SELDIprobe. Such articles are useful for purifying the biomarker or detectingthe biomarker.

8. Kits for Detection of Biomarkers for Ovarian Cancer

In another aspect, the present invention provides kits for qualifyingovarian cancer status, which kits are used to detect biomarkersaccording to the invention. In one embodiment, the kit comprises a solidsupport, such as a chip, a microtiter plate or a bead or resin having acapture reagent attached thereon, wherein the capture reagent binds abiomarker of the invention. Thus, for example, the kits of the presentinvention can comprise mass spectrometry probes for SELDI, such asProteinChip® arrays. In the case of biospecific capture reagents, thekit can comprise a solid support with a reactive surface, and acontainer comprising the biospecific capture reagent.

The kit can also comprise a washing solution or instructions for makinga washing solution, in which the combination of the capture reagent andthe washing solution allows capture of the biomarker or biomarkers onthe solid support for subsequent detection by, e.g., mass spectrometry.The kit may include more than type of adsorbent, each present on adifferent solid support.

In a further embodiment, such a kit can comprise instructions forsuitable operational parameters in the form of a label or separateinsert. For example, the instructions may inform a consumer about how tocollect the sample, how to wash the probe or the particular biomarkersto be detected.

In yet another embodiment, the kit can comprise one or more containerswith biomarker samples, to be used as standard(s) for calibration.

9. Determining Therapeutic Efficacy of Pharmaceutical Drug

In another embodiment, this invention provides methods for determiningthe therapeutic efficacy of a pharmaceutical drug. These methods areuseful in performing clinical trials of the drug, as well as monitoringthe progress of a patient on the drug. Therapy or clinical trialsinvolve administering the drug in a particular regimen. The regimen mayinvolve a single dose of the drug or multiple doses of the drug overtime. The doctor or clinical researcher monitors the effect of the drugon the patient or subject over the course of administration. If the drughas a pharmacological impact on the condition, the amounts or relativeamounts (e.g., the pattern or profile) of the biomarkers of thisinvention changes toward a non-disease profile. For example, hepcidin isincreased with disease, while transthyretin is decreased in disease.Therefore, one can follow the course of the amounts of these biomarkersin the subject during the course of treatment. Accordingly, this methodinvolves measuring one or more biomarkers in a subject receiving drugtherapy, and correlating the amounts of the biomarkers with the diseasestatus of the subject. One embodiment of this method involvesdetermining the levels of the biomarkers for at least two different timepoints during a course of drug therapy, e.g., a first time and a secondtime, and comparing the change in amounts of the biomarkers, if any. Forexample, the biomarkers can be measured before and after drugadministration or at two different time points during drugadministration. The effect of therapy is determined based on thesecomparisons. If a treatment is effective, then the biomarkers will trendtoward normal, while if treatment is ineffective, the biomarkers willtrend toward disease indications. If a treatment is effective, then thebiomarkers will trend toward normal, while if treatment is ineffective,the biomarkers will trend toward disease indications.

10. Use of Biomarkers for Ovarian Cancer in Screening Assays and Methodsof Treating Ovarian Cancer

The methods of the present invention have other applications as well.For example, the biomarkers can be used to screen for compounds thatmodulate the expression of the biomarkers in vitro or in vivo, whichcompounds in turn may be useful in treating or preventing ovarian cancerin patients. In another example, the biomarkers can be used to monitorthe response to treatments for ovarian cancer. In yet another example,the biomarkers can be used in heredity studies to determine if thesubject is at risk for developing ovarian cancer.

Compounds suitable for therapeutic testing may be screened initially byidentifying compounds which interact with hepcidin and one or morebiomarkers listed herein. By way of example, screening might includerecombinantly expressing a biomarker, purifying the biomarker, andaffixing the biomarker to a substrate. Test compounds would then becontacted with the substrate, typically in aqueous conditions, andinteractions between the test compound and the biomarker are measured,for example, by measuring elution rates as a function of saltconcentration. Certain proteins may recognize and cleave one or morebiomarkers of Table I, in which case the proteins may be detected bymonitoring the digestion of one or more biomarkers in a standard assay,e.g., by gel electrophoresis of the proteins.

In a related embodiment, the ability of a test compound to inhibit theactivity of one or more of the biomarkers may be measured. One of skillin the art will recognize that the techniques used to measure theactivity of a particular biomarker will vary depending on the functionand properties of the biomarker. For example, an enzymatic activity of abiomarker may be assayed provided that an appropriate substrate isavailable and provided that the concentration of the substrate or theappearance of the reaction product is readily measurable. The ability ofpotentially therapeutic test compounds to inhibit or enhance theactivity of a given biomarker may be determined by measuring the ratesof catalysis in the presence or absence of the test compounds. Theability of a test compound to interfere with a non-enzymatic (e.g.,structural) function or activity of hepcidin or another one or more ofthe biomarkers herein may also be measured. For example, theself-assembly of a multi-protein complex which includes hepcidin may bemonitored by spectroscopy in the presence or absence of a test compound.Alternatively, if the biomarker is a non-enzymatic enhancer oftranscription, test compounds which interfere with the ability of thebiomarker to enhance transcription may be identified by measuring thelevels of biomarker-dependent transcription in vivo or in vitro in thepresence and absence of the test compound.

Test compounds capable of modulating the activity of any of thebiomarkers of Table I may be administered to patients who are sufferingfrom or are at risk of developing ovarian cancer or other cancer. Forexample, the administration of a test compound which increases theactivity of a particular biomarker may decrease the risk of ovariancancer in a patient if the activity of the particular biomarker in vivoprevents the accumulation of proteins for ovarian cancer. Conversely,the administration of a test compound which decreases the activity of aparticular biomarker may decrease the risk of ovarian cancer in apatient if the increased activity of the biomarker is responsible, atleast in part, for the onset of ovarian cancer.

In an additional aspect, the invention provides a method for identifyingcompounds useful for the treatment of disorders such as ovarian cancerwhich are associated with increased levels of modified forms ofhepcidin. For example, in one embodiment, cell extracts or expressionlibraries may be screened for compounds which catalyze the cleavage offull-length hepcidin to form truncated forms of hepcidin. In oneembodiment of such a screening assay, cleavage of hepcidin may bedetected by attaching a fluorophore to hepcidin which remains quenchedwhen hepcidin is uncleaved but which fluoresces when the protein iscleaved. Alternatively, a version of full-length hepcidin modified so asto render the amide bond between amino acids x and y uncleavable may beused to selectively bind or “trap” the cellular protease which cleavesfull-length hepcidin at that site in vivo. Methods for screening andidentifying proteases and their targets are well-documented in thescientific literature, e.g., in Lopez-Ottin et al. (Nature Reviews,3:509-519 (2002)).

In yet another embodiment, the invention provides a method for treatingor reducing the progression or likelihood of a disease, e.g., ovariancancer, which is associated with the increased levels of truncatedhepcidin. For example, after one or more proteins have been identifiedwhich cleave full-length hepcidin, combinatorial libraries may bescreened for compounds which inhibit the cleavage activity of theidentified proteins. Methods of screening chemical libraries for suchcompounds are well-known in art. See, e.g., Lopez-Otin et al. (2002).Alternatively, inhibitory compounds may be intelligently designed basedon the structure of hepcidin.

Full-length hepcidin is believed to be involved in regulation of thebody's iron stores, as well as in hereditary hemochromatosis, chronicrenal insufficiency, and renal anemia. Hepcidin expression is alsoinduced as part of the body's immune response via the interleukingcascade. Because hepcidin is highly processed from its pre-pro andpro-forms, it is likely that there are proteases which target and cleaveit. Therefore, in a further embodiment, the invention provides methodsfor identifying compounds which increase the affinity of truncatedhepcidin for its target proteases. For example, compounds may bescreened for their ability to cleave hepcidin. Test compounds capable ofmodulating the cleavage of hepcidin or the activity of molecules whichinteract with hepcidin may then be tested in vivo for their ability toslow or stop the progression of ovarian and/or endometrial cancer in asubject.

At the clinical level, screening a test compound includes obtainingsamples from test subjects before and after the subjects have beenexposed to a test compound. The levels in the samples of one or more ofthe biomarkers listed in Table I may be measured and analyzed todetermine whether the levels of the biomarkers change after exposure toa test compound. The samples may be analyzed by mass spectrometry, asdescribed herein, or the samples may be analyzed by any appropriatemeans known to one of skill in the art. For example, the levels of oneor more of the biomarkers listed in Table I may be measured directly byWestern blot using radio- or fluorescently-labeled antibodies whichspecifically bind to the biomarkers. Alternatively, changes in thelevels of mRNA encoding the one or more biomarkers may be measured andcorrelated with the administration of a given test compound to asubject. In a further embodiment, the changes in the level of expressionof one or more of the biomarkers may be measured using in vitro methodsand materials. For example, human tissue cultured cells which express,or are capable of expressing, one or more of the biomarkers of Table Imay be contacted with test compounds. Subjects who have been treatedwith test compounds will be routinely examined for any physiologicaleffects which may result from the treatment. In particular, the testcompounds will be evaluated for their ability to decrease diseaselikelihood in a subject. Alternatively, if the test compounds areadministered to subjects who have previously been diagnosed with ovariancancer, test compounds will be screened for their ability to slow orstop the progression of the disease.

11. EXAMPLES 11.1. Example 1. Discovery of Biomarkers for Ovarian Cancer

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims.

Samples: Serum and urine samples were acquired from the MDACC Ovariancancer sample bank (MD Anderson). The samples had been collected fromcancer patients pre-operatively from years 2000 to 2004 and stored at−80° C. Sample distribution was as follows: ovarian cancer (OvCa), 200;endometrial cancer, 50; cervical cancer, 50 and benign, 50. Many, butnot all, of the OvCa and benign serum and urine samples were from thesame patient. The samples were run in two sets. The first set comprisedthe 200 ovarian cancer samples and 50 benign samples. This provided aninitial list of candidate biomarkers. To test the tumor-type specificityof these candidate biomarkers, a subset (50) of ovarian cancer samples,along with the 50 benign samples, and the 50 endometrial and 50 cervicalcancer samples were analyzed.

Serum Profiling: Serum profiling was performed only on the ovariancancer and benign samples. Randomized templates containing the samplesto be profiled were generated using the Ciphergen Express softwareprogram. Samples from the tumor bank were thawed on ice, added to a 96well plate (following the template for arrangement), and centrifuged for20 minutes at 4000 rpm. Aliquots of the serum were then put into fresh96 well plates and stored at −80° C. until use. Serum samples wereprofiled on IMAC-Cu⁺⁺ and on Q10 (see protocol below) on triplicateProteinChip Arrays. All replicates were prepared on the same day andwere read on a PBSIIc (beginning the morning following preparation)and/or a PCS4000 (beginning a few days after preparation). Arrays wereprocessed with sample using a Biomek 2000 robot.

IMAC-Cu serum profiling Protocol: Serum was first denatured by a ureatreatment. 5 μl serum was added to 7.5 μl 9M urea 2% CHAPS 50 mM TrisHCl pH 9 in a 96-well v-plate. The plate was covered with tape andshaken at Rm T for 20 minutes. 237.5 μl of binding buffer (50 mM sodiumphosphate buffer, 250 mM NaCl, pH 6.0) were added to each well (=1:50dilution) and mixed well. 50 μl of diluted serum was added to another96-well v-plate and 150 μl of binding buffer was added (=1:200dilution). IMAC arrays were pre-activated by adding 50 μl of 50 mM CuSO₄per well and incubated at RmT for 10 minutes without shaking. The arrayswere washed with 150 μl/spot of water by mixing up and down once, thenwashed with 50 μl of 50 mM NaAc pH 4 per spot, and incubated at RmT for5 minutes without shaking. They were then washed with 150 μl water/spottwice, by mixing up and down once each time.

IMAC30 chips were equilibrated twice with binding buffer. The chips wereincubated for 5 minutes each without shaking. 50 μl of 1:200 dilutedserum sample was added to each well and incubated at RmT for 120 minuteswithout shaking. The chips were washed three times with 150 μl ofbinding buffer per well and then “sip and spit” mixed three times,without shaking. The chips were washed with 150 μl of water twice withthree mixing cycles, and the bioprocessor reservoir was then removed.The water was aspirated off, and the chips were air dried for tenminutes. One μl of SPA matrix (12.5 mg/ml) in 50% acetonitrile/0.5% TFAwater was added per spot, and the chips were air dried for 10 minutes.The application was repeated, and the chips were air dried overnight.

Q-10 Serum profiling Protocol: The samples were diluted into bindingbuffer. Sample dilution: 1:250 patient serum sample dilution in 100 mMPhosphate buffer (PB), pH 7.0. The Q10 chips were pretreated twice with150 μl of 100 mM PB, pH 7.0, and incubated five minutes without shaking.50 μl of 1:250 diluted patient sample was added in each well. The chipswere spun at 900 rpm for 45 seconds in a centrifuge and incubated 120minutes at RmT without shaking. The chips were washed four times with150 μl of 100 mM PB, pH 7 per well, and three “sip and spit” mixes,without shaking for each wash. The chips were washed with 150 μl ofwater one time with three mixing cycles. The bioprocessor reservoir wasremoved, the water was removed, and the chips were air dried for tenminutes. One μl of SPA matrix (12.5 mg/ml) in 50% acetonitrile/0.5% TFAwater was added per spot, and the chips were air dried for 10 minutes.The application was repeated, and the chips were air dried overnight.

Urine Profiling: Urine profiling was performed on both the initial set(200 ovarian cancer samples and 50 benign samples) as well as the secondset (50 each ovarian cancer, benign, endometrial cancer, and cervicalcancer). Samples from the tumor bank were thawed on ice, added to a 96well plate (following the above mentioned template for arrangement), andcentrifuged for 20 minutes at 4000 rpm (urine had not been centrifugedprior to initial freezing and storage in the sample bank). Aliquots ofthe urine were then put into fresh 96 well plates and stored at −80° C.until use. Two runs of urine profiling were conducted. In the first run,only OvCa and benign samples were used. Samples were profiled on CM10and on IMAC-Cu++. The same randomized template that was generated forserum profiling was used; however, there were some samples for whichthere was not a matching patient urine sample and in these cases, adifferent patient sample was substituted. Urine samples were profiled(see protocol below) on duplicate ProteinChip Arrays. All replicateswere prepared on the same day and were read on a PBSIIc (beginning themorning following preparation) and/or a PCS4000 (beginning a few daysafter preparation). Arrays were processed with sample using a Biomek2000 robot. In the second run, endometrial, cervical, benign, and asubgroup of the original OvCa samples (from the first run) wereprofiled. Samples were profiled on CM10 and on IMAC-Cu⁺⁺. The originalrandomized template was again used, but 100 of the OvCa samples weresubstituted with endometrial or cervical urine samples and only 50 ofthe original set of OvCa samples were included.

CM10 Urine profiling Protocol: 15 μL of urine sample were added to 23 μLof denaturing buffer (9 M urea/2% CHAPS) and ncubated for 30 min at 4°C. 263 μl of binding buffer (BB), 100 mM Sodium Acetate pH 4, was addedto each denatured sample and mixed well. The chip surface was preparedwith two five-minute BB washes. The buffer was removed, and 150 μl ofthe diluted urine sample was added to each well and incubated at RT withshaking for 30 minutes. The sample was removed and replaced with a fresh125 μl of the same diluted sample on the appropriate spot, and thenincubated at RT with shaking for 30 minutes. The sample was removed, andthe chip was washed with thee five-minute BB washes. The buffer wasremoved, and the chip was washed with water quickly (no incubation) twotimes. The bioprocessor reservoir was removed, and the chip was airdried. One μl of SPA matrix (12.5 mg/ml) in 50% acetonitrile/0.5% TFAwater was added per spot, and the chip was air day for 10 minutes. Theapplication was repeated, and the chips were air dried overnight.

IMAC_Cu⁺⁺ Urine profiling Protocol: 15 μL of urine sample were added to23 μL of denaturing buffer (9M urea/2% CHAPS). The samples wereincubated for 30 minutes at 4° C. 263 ul of binding buffer (BB), 100 mMSodium Phosphate+0.5 M NaCl pH 7, were added to each denatured sampleand mixed well. The IMAC chips were prepared with copper by adding 50 ulof 50 mM CuSO₄ per well and incubated at RmT for 10 minutes. The chipswere washed with 150 ul/spot of water once for two minutes and incubatedwith 50 ul of 50 mM NaAc pH4 per spot for five minutes, then washed withwater using 150 ul/spot water once for two minutes. The water wasremoved, and the IMAC30 chips were equilibrated twice for five minuteswith binding buffer BB. The buffer was removed, and 150 ul diluted urinesample was added. The chips were incubated at RT with shaking for 30minutes. The sample was removed and replaced with a fresh 125 ul of thesame diluted sample on the appropriate spot, and the chips wereincubated at RT with shaking for 30 minutes. The sample was removed, andthe chips were washed with BB three times for five minutes. The bufferwas removed, and the chips were washed quickly with water twice (noincubation). The bioprocessor reservoir was removed, and the chips wereair dried. One ul of SPA matrix (12.5 mg/ml) in 50% acetonitrile/0.5%TFA water was added per spot, and the chips were air dried for 10minutes. The application was repeated, and the chips were air driedovernight.

Data analysis: Data were acquired using CiphergenExpress software. Masscalibration was performed using external calibrants, intensitynormalization was based on total ion current using an externalnormalization factor, and baseline subtraction was performed. Peakdetection was performed in CiphergenExpress software using the criteriathat a peak must have a signal/noise ratio of 3:1 and be present in 20%of the spectra. Statistical analysis was performed in CiphergenExpresssoftware using the Mann-Whitney test (for two groups, e.g. benign versusovarian cancer) or Kruskal-Wallis test (for multiple group comparison,e.g. benign versus ovarian cancer vs endometrial cancer).

Results: The data from the analysis of urine samples (200 ovarian cancerand 50 benign disease) were analyzed first.

TABLE 2 Peaks from with p < .05 using the Mann-Whitney test, whencomparing benign versus ovarian cancer. Array p value AUC m/z IMAC302.52E−04 0.664118 2785.654 IMAC30 8.01E−04 0.642618 2187.061 IMAC300.002658 0.642618 2431.063

An AUC>0.5 indicates that the peak is greater in the ovarian cancergroup than in the benign group, while an AUC<0.5 indicates that the peakis lower in the ovarian cancer group than in the benign group.

TABLE 3 Peaks with p < .05 for the respective comparisons. p values:ovarian vs Median intensity Condition Baseline mass benign cervicalendometrial benign cervical endometrial ovarian Ovarian and endometrialmarkers IMAC low 5 2193.623 0.0023 0.0211 0.5997 10.83 15.54 23.58 28.63CM10 low 5 2194.852 0.0061 0.0012 0.0855 21.61 20.34 29.83 47.21 CM10low 5 2434.871 0.002 0.0936 0.5628 5.459 13.16 16.13 22.21 IMAC low 52437.971 0.0619 IMAC low 5 2664.621 0.01 0.0045 0.1311 1.468 1.437 1.7552.076 CM10 low 5 2664.983 0.0001 0.0001 0.0132 4.211 3.612 7.373 10.96IMAC low 5 2792.399 0.0011 0.0311 0.9721 2.37 5.568 10.73 13.55 CM10 low5 2793.231 0.002 0.0004 0.0578 7.158 5.549 14.51 40.42 Ovarian andendometrial cancer specific markers are defined as peaks with p values<.05 for the comparisons versus benign disease and cervical cancer.

To determine the specificity of these peaks for ovarian cancer, urinesamples from a variety of gynecological cancers (50 ovarian cancer, 50endometrial cancer, and 50 cervical cancer) and benign pelvic disease(n=50) were profiled. Profiling and data analysis were performed as forthe first set, except that the Kruskal-Wallis test was used to test forsignificance among multiple groups.

Analysis of data obtained from serum samples was performed as for theurine samples. Table 3 shows significant peaks (p<0.05, using theMann-Whitney test to compare benign versus ovarian cancer) obtained fromserum analysis using the IMAC ProteinChip array. As above, an AUC<0.5indicates that the peak is down-regulated in ovarian cancer while anAUC>0.5 indicates that the peak is up-regulated in ovarian cancer. Thesepeaks were confirmed to represent forms of hepcidin disclosed above. Thepeak at 2789.4 is hepcidin-25.

TABLE 4 Mass P value AUC ID comments 2789.4 0.0000069 0.70 hepcidin

11.2. Purification and Identification of 2789 from MD Anderson UrineSample

Urine samples were acquired from the MDACC Ovarian cancer sample bank.The samples had been collected from cancer patients pre-operatively fromyears 2000 to 2004 and stored at −80° C. Sample distribution was asfollows: ovarian cancer (OvCa), 200; endometrial cancer, 50; cervicalcancer, 50 and benign, 50. The samples were run in two sets. Experiment#1: The first set comprised the 200 ovarian cancer samples and 50 benignsamples. This provided an initial list of candidate biomarkers. Samplesprofiled on both CM10 and IMAC-Cu. Experiment #2: To test the tumor-typespecificity of these candidate biomarkers, a subset (50) of ovariancancer samples, along with the 50 benign samples, and the 50 endometrialand 50 cervical cancer samples were analyzed. Samples profiled on bothCM10 and IMAC-Cu.

1.0 ml of urine was added to 375 ul of IMAC HyperCel (Biosepra) beadswhich were pre-loaded with copper and incubated at 4° C. for 1 hour. Thebeads were washed with 350 ul of 100 mM NaPO₄, pH7 once, 100 mM NaAc,pH5 twice and organic solvent (33.3% acetonitrile, 16.7% isopropanol and0.1% TFA) twice. The majority of the 2789 Da marker was present in theorganic wash. 5 ul was applied onto NP20 chip (Ciphergen C553-0043). TheNP20 chip with 2789 Da on it were treated with SPA (Ciphergen C300-0002)and loaded onto a MicroMass Q-TOF which was equipped with a ProteinChipInterface (Ciphergen Z200-0003). Ions were created using a pulsednitrogen laser (Laser Science Inc. VSL 337 NDS, Franklin, Mass.)operated at 30 pulses per second delivering an average pulse fluence of130 mJ/mm². Nitrogen gas, at 10 millitorr of pressure, was used forcollisional cooling of formed ions and argon was used as collision gasfor all low energy collision-induced dissociation experiments. Thepreviously described CHCA matrix system was used for tandem analysis ofthe acid hydrolysis products. Applied collision energy general followedthe rule of 50 eV/kD, and each acquisition was typically the sum offive-minute of spectra. For MS and MS/MS modes, the system wasexternally calibrated using a mixture of known peptides. The CIDspectrum was smoothed and centroided and exported as a sequest file.Protein identification was carried out using Matrix Science MascotProgram (available online at http://www.matrixscience.com).

11.3. Purification and Identification of 2789 from JHU Serum Sample

A total of 178 archived serum specimens were collected at the JohnsHopkins Medical Institutions with institutional approval. The sample setincluded specimens from 40 healthy women (age, mean±SD, 42±7 years), 40patients with stage III/IV (23/17 cases) ovarian cancer (age, mean±SD,56±14 years), groups of 19 patients each with stage 0/I/II/III (3/5/8/3cases) breast (age, mean±SD, 54±15 years) or stage I/II/III (1/10/8cases) colon cancers (age, mean±SD, 69±16 years), and groups of 20patients each with stage I/II/III (1/12/7 cases) prostate (age, mean±SD,58±8 years), stage II/III (4/16 cases) pancreatic cancers (age, mean±SD,66±8 years), or diabetes (age, mean±SD, 52±18 years). All patients withbreast, colon, pancreatic cancers, and diabetes were female. All aboveserum samples were processed promptly after collection and stored at−70° C. until use. Additionally, 3 pairs of plasma and serum samplesfrom 3 patients with stage III/IV ovarian cancer (age, mean±SD, 57±13years) and one serum sample from a healthy control were freshlycollected and immediately processed. BD Plus Plastic K2EDTA tubes wereused for plasma preparation. All specimens were obtained beforetreatment and before surgery.

7.5 mg of rabbit anti-ITIH4 antibody (custom made antibody specificagainst MNFRPGVLSSRQLGLPGPPDVPDHAAYHPF (SEQ ID NO: 26)) was linked to12.5 ml of AminoLink® coupling bead (Pierce P/N 20501B). 70 ul serumsample were diluted with 630 ul of PBS pH 7.2 with 0.05% Tween andloaded onto 70 ul of anti-ITIH4 beads. After incubation at 4° C.overnight, the beads were washed with 1 ml of PBS+0.1% Tween bufferthree times followed by 1 ml of water wash once. The beads were theneluted with 50 ul of organic elution (33.3% isopropanol/16.7%acetonitrile/0.1% trifluroacetic acid) three times. Flow through andthree PBS washes along with organic eluents were profiled ontoIMAC30-copper ProteinChip arrays (Ciphergen C553-0078) using a PBS IIProteinChip reader. The 2789 Da protein was present in the flow throughas well as all three PBS and organic eluents. The flow through fractionfrom the IP was loaded onto YM10 membrane (Millipore 42407) and 50 ul offlow through fraction from YM10 was profiled on IMAC 30 copper(Ciphergen C553-0078) arrays The IMAC30-copper arrays were treated withSPA (Ciphergen C300-0002) and load onto Q-TOF which was equipped withCiphergen Interface (Ciphergen z200-0003) for tandem mass spectrometry.In addition, 800 ul eluent from the IP was loaded onto a YM10 membrane(Millipore 42407). The flow through fraction from the YM10 membrane wasdried down and rehydrated in 50 ul of 50% acetonitile. 1 ul of thisconcentrated eluent was applied onto NP 20 chips with SPA as matrix.This NP20 chips were load onto Q-TOF which was equipped with CiphergenInterface for tandem mass spectrometry. For MS/MS experiments, spectrawere acquired on a Micromass QTOF II tandem mass spectrometer equippedwith a Ciphergen (Fremont, Calif.) ProteinChip Array interface(ProteinChip Qq-TOF). Ions were created using a pulsed nitrogen Laser,Laser Science Inc. VSL 337 NDS, (Franklin, Mass.) operated at 30 pulsesper second delivering an average pulse fluence of 130 mJ/mm². Nitrogengas, at 10 millitorr of pressure, was used for collisional cooling offormed ions and argon was used as collision gas for all low energycollision-induced dissociation experiments. The previously describedCHCA matrix system was used for tandem analysis of the acid hydrolysisproducts. Applied collision energy general followed the rule of 50eV/kD, and each acquisition was typically the sum of five-minute ofspectra. For MS and MS/MS modes, the system was externally calibratedusing a mixture of known peptides. The CID spectrum was smoothed andcentroided and exported as a sequest file. Protein identification wascarried out using Matrix Science Mascot Program (available on line athttp://www.matrixscience.com).

11.4. Purification and Identification of 2789 from MD Anderson SerumSample

160 ul serum samples were denatured with 240 ul of 9M Urea, 50 mM Tris,pH9, 2% CHAPS at 4° C. for 20 minutes. The denatured serum was loadedonto 720 ul of Q-hyper-DF beads (Biosepra 20078) and incubated at 4° C.for 40 minutes. Twelve fractions were collected in a decreasing stepwisepH gradient. The fractions were profiled on CM10 ProteinChip arrays(Ciphergen c553-007) using a PBS II ProteinChip reader and 2789 Daprotein was presented in the flow through. The pH of floe through wasthen adjusted to pH4 by 1M HOAc and 50 ul was loaded onto CM10 chip.This CM10 chips were treated with SPA (Ciphergen C300-0002) and loadonto Q-TOF which was equipped with Ciphergen Interface (CiphergenZ200-0003) for tandem mass spectrometry. For MS/MS experiments, spectrawere acquired on a Micromass QTOF II tandem mass spectrometer equippedwith a Ciphergen (Fremont, Calif.) ProteinChip Array interface(ProteinChip Qq-TOF). Ions were created using a pulsed nitrogen Laser,Laser Science Inc. VSL 337 NDS (Franklin, Mass.) operated at 30 pulsesper second delivering an average pulse fluence of 130 mJ/mm². Nitrogengas, at 10 millitorr of pressure, was used for collisional cooling offormed ions and argon was used as collision gas for all low energycollision-induced dissociation experiments. The previously describedCHCA matrix system was used for tandem analysis of the acid hydrolysisproducts. Applied collision energy general followed the rule of 50eV/kD, and each acquisition was typically the sum of five-minute ofspectra. For MS and MS/MS modes, the system was externally calibratedusing a mixture of known peptides. The CID spectrum was smoothed andcentroided and exported as a sequest file. Protein identification wascarried out using Matrix Science Mascot Program (available online athttp://www.matrixscience.com).

11.5. Example 2. Discovery of Biomarkers for Ovarian Cancer

Previous work identified a fragment of inter-alpha trypsin inhibitorheavy chain 4 (ITIH4, also sometimes referred to as ITIH4) as abiomarker with discriminatory power in detecting ovarian cancer. In thisexample, the correlation networks constructed using expression data ofproteins in clinical serum samples that co-precipitate with ITIH4fragment were analyzed.

The first analysis was done using a panel of 142 serum samples (41 withovarian cancer, 41 healthy controls, and 20 each with breast,colorectal, and prostate cancers). Expression data were generated intriplicate through immunoprecipitation/pull-down using a polyclonalantibody generated against the ITIH4 3272 m/z fragment, followed bysurface-enhanced laser desorption/ionization mass spectrometry. Forvalidation, samples from two additional sites were similarly processed.The first set consisted of 114 ovarian cancer samples (16 pretreatment,17 post-treatment, 37 cancer-free monitoring cases, and 30 recurrentcases). The second set had 11 ovarian cancer cases, 16 benign cases, and30 healthy controls.

Using correlation network analysis, in addition to the ITIH4 fragments,a group of four peaks was discovered that were upregulated and highlycorrelated among 41 ovarian cancer cases, yet under-expressed andminimally correlated among the healthy controls and the other cancersamples. These peaks were further identified as variants of hepcidin.FIG. 2 shows the sequences of various hepcidin fragments, including thefour correlated fragments, hepcidin-25, hepcidin-24, hepcidin-22, andhepcidin-20.

Among the 142 samples, receiver-operating-characteristic (ROC) curveanalysis showed that the peak corresponding to the full-length hepcidinhad an area-under-curve (AUC) of 0.876 (95% CI: 0.795-0.957) inseparating ovarian cancer from healthy controls (see FIG. 1) and 0.774(0.678-0.871) in separating ovarian cancer from the other three types ofcancers (see FIG. 4). In the first validation set, hepcidin was higherin the pretreatment and recurrent groups than in the post-treatment andcancer-free monitoring groups (AUC=0.756 (0.702-0.811)), with therecurrent cases having the highest hepcidin levels (see FIG. 5). In thesecond validation set, the AUC was 0.722 (0.693-0.851) in separatingovarian cancer from benign and healthy controls (for both validationsets, all triplicates were included in the analysis; see FIG. 5).Preliminary results indicated that hepcidin and ITIH4 fragment arebinding partners.

In the first approach, 10 ul of serum was added to 10 ul of ITIH4 beadsin 90 ul PBS with 0.05% Triton. The beads were incubated overnight at 4°C. At this stage, the flow-through (104 ul) was removed, and 5 ul wasanalyzed on IMAC and CM10 chips. The beads were washed three times with150 ul PBS with 0.1% Triton. After each wash, 20 ul wash buffer wasremoved and analyzed on IMAc and CM10 chips. The beads were eluted aftereach wash with 50 ul organic elution buffer. 50 ul of the total eluentwas then analyzed on IMAc and CM10 chips. In a second approach, samplesfrom the ITIH4 IP flow through fraction A13 were concentrated andanalyzed on IMAC chips.

FIG. 3 shows the SELDI spectrum of the serum sample afterimmunoprecipitation/pull-down using the antibody against ITIH4 fragment.Correlation network analysis showed that the four peaks (m/z 2191, 2436,and 2788) are highly correlated among themselves and inverselycorrelated with the group of ITIH4 fragments in serum samples fromovarian cancer patients. The correlation is not as strong among healthycontrols. Similar correlation network analysis was performed betweenovarian cancer, prostate cancer, breast cancer, and healthy controls.The strongest correlation among the four peaks was the one with ovariancancer. FIG. 6 shows a scatterplot of the five groups of samples in twoof the four peaks representing hepcidin variants. FIG. 7 shows ascatterplot of five groups of patients from an independent validationset using two of the hepcidin peaks. It shows that these peaks are lowerin patients free of cancer and patients after treatment, and are higherin patients with ovarian cancer pretreatment, as well as in those withrecurrent ovarian cancer. The hepcidin level correlates with the tumorload. FIG. 8 shows a scatterplot of five groups of patients from asecond independent validation set using two of the hepcidin peaks. Itshows that these peaks are lower in healthy controls and patients withbenign diseases, and are higher in patients with ovarian cancer.

11.6. Example 3. Biomarker Assay Using Large Sample Set

To further evaluate the quality of hepcidin as an ovarian cancer marker,a large multi-institutional study was performed. A total of 607 serumsamples from five sites were analyzed using SELDI TOF-MS protocolsoptimized for the seven biomarkers. They included 234 women with benigngynecologic diseases, and 373 patients with invasive epithelial ovariancancer (101 early stage, 231 late stage, and 40 stage unknown). Amongthem, 165 benigns and 228 cancers had a CA125 available at time ofanalysis. The median and quartiles of CA125 for benign, early stage, andlate or unknown stage were 26/11/57 IU, 80/22/434 IU, and 234/40/1114IU, respectively. The biomarkers were assessed individually using theMann-Whitney U Test. A linear composite index was derived in anunsupervised fashion using data from one site aria then calculated forthe remaining data using the fixed formula. ROC curve analyses wereperformed on data from individual sites and all sites combined.

A total of 607 serum samples from five sites were analyzed using SELDITOF-MS protocols optimized for seven biomarkers: hepcidin-25 (M2789),cysteinylated transthyretin, Apo A1 (M28043), transferrin (M79K),CTAP-III (M9313.9), ITIH4 fragment 1 (M3272) and β2-microglobulin(M11.7K) (“the seven marker panel”). They included 234 women with benigngynecologic diseases, and 373 patients with invasive epithelial ovariancancer (101 early stage, 231 late stage, and 40 stage unknown). Amongthem, 165 benigns and 228 cancers had a CA125 available at time ofanalysis. The median and quartiles of CA125 for benign, early stage, andlate or unknown stage were 26/11/57 IU, 80/22/434 IU, and 34/40/1114 IU,respectively. The biomarkers were assessed individually using theMann-Whitney U Test. A linear composite index was derived in anunsupervised fashion using data from one site and then calculated forthe remaining data using the fixed formula. ROC curve analyses wereperformed on data from individual sites and all sites combined. Allseven biomarkers individually demonstrated statistically significantdifferentiating power, and the majority had p-value<0.00001. AUCs of thecomposite index in ROC analyses for the six sites were 0.602, 0.566,0.821, 0.813, and 0.592 in detecting cancer at all stages from benign.On the combined data, the differences in AUC between the index and CA125were not statistically significant for the detection of cancer at allstages (AUC=0.706 vs. 0.725) or early stages only (AUC=0.534 vs. 0.653).However, the index did better at the high-sensitivity range. At a fixedsensitivity of 86%, the specificity of the index was 34% (77/226)compared to CA125 at 26% (42/163). For early stage cases, at a fixedsensitivity of 84%, the specificity of the index was 24% (55/226)compared to CA125 at 14%% (22/163).

11.7. Example 4. Biomarker Assay Using Smaller Sample Set

Pre-operative serum samples from 202 consecutive patients beingevaluated for ovarian pathology were aliquotted, and frozen within sixhours of collection. The serum samples were evaluated using aSELDI-TOF-MS proteomics assay for the seven marker panel. 126 sampleswere used to train a model and the remaining samples were used forblinded testing. Of the 202 patients, 132 had benign disease (includingendometriosis, benign pelvic cyst, uterine fibromas), 11 had borderlinetumors, 50 had invasive epithelial ovarian cancer, 3 had germ celltumors, and the remaining had metastatic non-gynecologic cancers. Themedian age in the benign disease group was 48.3 years (range 20-84), and65.1 years (range 40-89) in the invasive ovarian cancer group.

Pre-operative serum samples from 202 consecutive patients beingevaluated for ovarian pathology were aliquotted, and frozen within sixhours of collection. The serum samples were evaluated using aSELDI-TOF-MS proteomics assay for the seven marker panel. 126 sampleswere used to train a model and the remaining samples were used forblinded testing. Of the 202 patients, 132 had benign disease (includingendometriosis, benign pelvic cyst, uterine fibromas), 11 had borderlinetumors, 50 had invasive epithelial ovarian cancer, 3 had germ celltumors, and the remaining had metastatic non-gynecologic cancers. Themedian age in the benign disease group was 48.3 years (range 20-84), and65.1 years (range 40-89) in the invasive ovarian cancer group. In thetraining set, CA125 had a sensitivity of 100% (95% CI: 88.1-100.0%) andspecificity of 63.3% (95% CI: 52.2-73.3%), while in the test set, CA125had a sensitivity of 95.0% (95% CI: 75.1-99.9%) and specificity of 67.5%(95% CI: 50.9-81.4%). A multivariable algorithm incorporating the sevenmarkers and CA125 had a sensitivity of 86.2% (95% CI: 68.3-96.1%) andspecificity of 94.4% (95% CI: 87.5-98.2%) in the training set and asensitivity of 80.0% (95% CI: 68.3-96.1%) and specificity of 90.0% (95%CI: 76.4-97.2%) in the test set. The seven marker panel may be useful inhelping triage patients being evaluated for a persistent pelvic mass.This marker panel improves specificity of CA125, although diminishes itssensitivity.

For the following panels, the sensitivity is 85.2% with 95% CI65.4%˜95.1%; the specificity is 96.7% with 95% CI 89.9%˜99.1%.

-   -   3 markers:    -   Apo, transthyretin, ITIH4    -   Apo, transthyretin, transferrin    -   4 markers:    -   Apo, transthyretin, ITIH4, transferrin    -   Apo, transthyretin, ITIH4, CTAP-III    -   5 markers:    -   Apo, transthyretin, ITIH4, transferrin, CTAP-III    -   Apo, transthyretin, ITIH4, transferrin, β2 microglobulin    -   6 markers:    -   Apo, transthyretin, ITIH4, transferrin, CTAP-III, β2        microglobulin

For the following panels, the sensitivity is 81.5% with 95% CI61.3%˜93.0%; the specificity is 97.8% with 95% CI 91.4%˜99.6%.

-   -   3 markers:    -   Apo, transthyretin, hepcidin    -   4 markers:    -   Apo, transthyretin, hepcidin, transferrin    -   Apo, transthyretin, hepcidin, CTAP-III    -   5 markers:    -   Apo, transthyretin, hepcidin, transferrin, CTAP-III    -   Apo, transthyretin, hepcidin, transferrin, β2 microglobulin    -   6 markers:    -   Apo, transthyretin, hepcidin, transferrin, CTAP-III, β2        microglobulin

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference in theirentirety for all purposes.

1. A method for qualifying ovarian cancer status in a subjectcomprising: (a) measuring one or more biomarkers in a biological samplefrom the subject, wherein at least one biomarker is hepcidin; and (b)correlating the measurement or measurements with an ovarian cancerstatus selected from ovarian cancer and non-ovarian cancer.
 2. Themethod of claim 1, comprising measuring a plurality of biomarkers in thebiological sample, wherein the plurality of biomarkers further comprisestransthyretin.
 3. The method of claim 2, wherein the plurality ofbiomarkers further comprises at least one biomarker selected from thegroup consisting of: Apo A1, transferrin, CTAP-III and ITIH4 fragment.4. The method of claim 3, wherein the plurality of biomarkers furthercomprises at least two biomarkers selected from the group consisting of:Apo A1, transferrin, CTAP-III and ITIH4 fragment.
 5. The method of claim3, wherein the plurality of biomarkers further comprises at least threebiomarkers selected from the group consisting of: Apo A1, transferrin,CTAP-III and ITIH4 fragment.
 6. The method of claim 3, wherein theplurality of biomarkers further comprises Apo A1, transferrin, CTAP-IIIand ITIH4 fragment.
 7. The method of claim 3, wherein hepcidin ishepcidin-25, transthyretin is cysteinylated transthyretin, and ITIH4fragment is ITIH4 fragment
 1. 8. The method of claim 3, wherein theplurality of biomarkers further comprises β-2 microglobulin. 9.-10.(canceled)
 11. The method of claim 1, wherein the at least one biomarkeris measured by immunoassay.
 12. The method of any of claim 1, whereinthe sample is blood or a blood derivative, ovarian cyst fluid, ascites,or urine. 13.-17. (canceled)
 18. The method of claim 1, whereinnon-ovarian cancer is a gynecological condition selected from benignovarian cyst, endometriosis, uterine fibroma, breast cancer and cervicalcancer. 19.-26. (canceled)
 27. A kit comprising: (a) a solid supportcomprising at least one capture reagent attached thereto, wherein thecapture reagent binds hepcidin; and (b) instructions for using the solidsupport to detect hepcidin. 28.-29. (canceled)
 30. A kit comprising: (a)at least one solid support comprising at least one capture reagentattached thereto, wherein the capture reagent binds or reagents bindhepcidin and transthyretin; and (b) instructions for using the solidsupport or supports to detect hepcidin and transthyretin. 31.-43.(canceled)